a3-decision-trees.ipynb

{"cells":[{"metadata":{},"cell_type":"markdown","source":"<center>\n<img src=\"https://habrastorage.org/files/fd4/502/43d/fd450243dd604b81b9713213a247aa20.jpg\">\n    \n## [mlcourse.ai](https://mlcourse.ai) - Open Machine Learning Course\n\nAuthors: [Maria Sumarokova](https://www.linkedin.com/in/mariya-sumarokova-230b4054/), and [Yury Kashnitsky](https://www.linkedin.com/in/festline/). Translated and edited by Gleb Filatov, Aleksey Kiselev, [Anastasia Manokhina](https://www.linkedin.com/in/anastasiamanokhina/), [Egor Polusmak](https://www.linkedin.com/in/egor-polusmak/), and [Yuanyuan Pao](https://www.linkedin.com/in/yuanyuanpao/). All content is distributed under the [Creative Commons CC BY-NC-SA 4.0](https://creativecommons.org/licenses/by-nc-sa/4.0/) license."},{"metadata":{"collapsed":true},"cell_type":"markdown","source":"# <center> Assignment #3 (demo)\n## <center>  Decision trees with a toy task and the UCI Adult dataset \n\nSame assignment as a [Kaggle Kernel](https://www.kaggle.com/kashnitsky/a3-demo-decision-trees) + [solution](https://www.kaggle.com/kashnitsky/a3-demo-decision-trees-solution). Fill in the answers in the [web-form](https://docs.google.com/forms/d/1wfWYYoqXTkZNOPy1wpewACXaj2MZjBdLOL58htGWYBA/edit)."},{"metadata":{},"cell_type":"markdown","source":"Let's start by loading all necessary libraries:"},{"metadata":{"trusted":true},"cell_type":"code","source":"%matplotlib inline\nfrom matplotlib import pyplot as plt\nplt.rcParams['figure.figsize'] = (10, 8)\nimport seaborn as sns\nimport numpy as np\nimport pandas as pd\nfrom sklearn.preprocessing import LabelEncoder\nimport collections\nfrom sklearn.model_selection import GridSearchCV\nfrom sklearn import preprocessing\nfrom sklearn.tree import DecisionTreeClassifier, export_graphviz\nfrom sklearn.ensemble import RandomForestClassifier\nfrom sklearn.metrics import accuracy_score\nfrom ipywidgets import Image\nfrom io import StringIO\nimport pydotplus #pip install pydotplus","execution_count":1,"outputs":[{"output_type":"stream","text":"/opt/conda/lib/python3.6/site-packages/sklearn/ensemble/weight_boosting.py:29: DeprecationWarning: numpy.core.umath_tests is an internal NumPy module and should not be imported. It will be removed in a future NumPy release.\n  from numpy.core.umath_tests import inner1d\n","name":"stderr"}]},{"metadata":{},"cell_type":"markdown","source":"### Part 1. Toy dataset \"Will They? Won't They?\""},{"metadata":{},"cell_type":"markdown","source":"Your goal is to figure out how decision trees work by walking through a toy problem. While a single decision tree does not yield outstanding results, other performant algorithms like gradient boosting and random forests are based on the same idea. That is why knowing how decision trees work might be useful."},{"metadata":{},"cell_type":"markdown","source":"We'll go through a toy example of binary classification - Person A is deciding whether they will go on a second date with Person B. It will depend on their looks, eloquence, alcohol consumption (only for example), and how much money was spent on the first date."},{"metadata":{},"cell_type":"markdown","source":"#### Creating the dataset"},{"metadata":{"trusted":true},"cell_type":"code","source":"# Create dataframe with dummy variables\ndef create_df(dic, feature_list):\n    out = pd.DataFrame(dic)\n    out = pd.concat([out, pd.get_dummies(out[feature_list])], axis = 1)\n    out.drop(feature_list, axis = 1, inplace = True)\n    return out\n\n# Some feature values are present in train and absent in test and vice-versa.\ndef intersect_features(train, test):\n    common_feat = list( set(train.keys()) & set(test.keys()))\n    return train[common_feat], test[common_feat]","execution_count":2,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"features = ['Looks', 'Alcoholic_beverage','Eloquence','Money_spent']","execution_count":3,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"#### Training data"},{"metadata":{"trusted":true},"cell_type":"code","source":"df_train = {}\ndf_train['Looks'] = ['handsome', 'handsome', 'handsome', 'repulsive',\n                         'repulsive', 'repulsive', 'handsome'] \ndf_train['Alcoholic_beverage'] = ['yes', 'yes', 'no', 'no', 'yes', 'yes', 'yes']\ndf_train['Eloquence'] = ['high', 'low', 'average', 'average', 'low',\n                                   'high', 'average']\ndf_train['Money_spent'] = ['lots', 'little', 'lots', 'little', 'lots',\n                                  'lots', 'lots']\ndf_train['Will_go'] = LabelEncoder().fit_transform(['+', '-', '+', '-', '-', '+', '+'])\n\ndf_train = create_df(df_train, features)\ndf_train","execution_count":4,"outputs":[{"output_type":"execute_result","execution_count":4,"data":{"text/plain":"   Will_go        ...         Money_spent_lots\n0        0        ...                        1\n1        1        ...                        0\n2        0        ...                        1\n3        1        ...                        0\n4        1        ...                        1\n5        0        ...                        1\n6        0        ...                        1\n\n[7 rows x 10 columns]","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Will_go</th>\n      <th>Looks_handsome</th>\n      <th>Looks_repulsive</th>\n      <th>Alcoholic_beverage_no</th>\n      <th>Alcoholic_beverage_yes</th>\n      <th>Eloquence_average</th>\n      <th>Eloquence_high</th>\n      <th>Eloquence_low</th>\n      <th>Money_spent_little</th>\n      <th>Money_spent_lots</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"metadata":{},"cell_type":"markdown","source":"#### Test data"},{"metadata":{"trusted":true},"cell_type":"code","source":"df_test = {}\ndf_test['Looks'] = ['handsome', 'handsome', 'repulsive'] \ndf_test['Alcoholic_beverage'] = ['no', 'yes', 'yes']\ndf_test['Eloquence'] = ['average', 'high', 'average']\ndf_test['Money_spent'] = ['lots', 'little', 'lots']\ndf_test = create_df(df_test, features)\ndf_test","execution_count":5,"outputs":[{"output_type":"execute_result","execution_count":5,"data":{"text/plain":"   Looks_handsome        ...         Money_spent_lots\n0               1        ...                        1\n1               1        ...                        0\n2               0        ...                        1\n\n[3 rows x 8 columns]","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Looks_handsome</th>\n      <th>Looks_repulsive</th>\n      <th>Alcoholic_beverage_no</th>\n      <th>Alcoholic_beverage_yes</th>\n      <th>Eloquence_average</th>\n      <th>Eloquence_high</th>\n      <th>Money_spent_little</th>\n      <th>Money_spent_lots</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"# Some feature values are present in train and absent in test and vice-versa.\ny = df_train['Will_go']\ndf_train, df_test = intersect_features(train=df_train, test=df_test)\ndf_train","execution_count":6,"outputs":[{"output_type":"execute_result","execution_count":6,"data":{"text/plain":"   Alcoholic_beverage_yes          ...            Alcoholic_beverage_no\n0                       1          ...                                0\n1                       1          ...                                0\n2                       0          ...                                1\n3                       0          ...                                1\n4                       1          ...                                0\n5                       1          ...                                0\n6                       1          ...                                0\n\n[7 rows x 8 columns]","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Alcoholic_beverage_yes</th>\n      <th>Money_spent_lots</th>\n      <th>Looks_handsome</th>\n      <th>Eloquence_average</th>\n      <th>Eloquence_high</th>\n      <th>Looks_repulsive</th>\n      <th>Money_spent_little</th>\n      <th>Alcoholic_beverage_no</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>0</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"df_test","execution_count":7,"outputs":[{"output_type":"execute_result","execution_count":7,"data":{"text/plain":"   Alcoholic_beverage_yes          ...            Alcoholic_beverage_no\n0                       0          ...                                1\n1                       1          ...                                0\n2                       1          ...                                0\n\n[3 rows x 8 columns]","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Alcoholic_beverage_yes</th>\n      <th>Money_spent_lots</th>\n      <th>Looks_handsome</th>\n      <th>Eloquence_average</th>\n      <th>Eloquence_high</th>\n      <th>Looks_repulsive</th>\n      <th>Money_spent_little</th>\n      <th>Alcoholic_beverage_no</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"metadata":{},"cell_type":"markdown","source":"#### Draw a decision tree (by hand or in any graphics editor) for this dataset. Optionally you can also implement tree construction and draw it here."},{"metadata":{},"cell_type":"markdown","source":"1\\. What is the entropy $S_0$ of the initial system? By system states, we mean values of the binary feature \"Will_go\" - 0 or 1 - two states in total."},{"metadata":{"trusted":true},"cell_type":"code","source":"# you code here\nnum_will_go = 0                    #0 means will go\nnum_will_not_go = 0                # 1 means will not go\nfor index, value in y.items():\n    if value == 0:\n        num_will_go +=1\n    else:\n        num_will_not_go += 1\nsize = y.size\n\np0 = num_will_go/size\np1 = num_will_not_go/size\n\ns0 = -p0 * np.log2(p0) - (p1) * np.log2(p1)  #Entropy calculation\n\nprint(s0)\n    ","execution_count":8,"outputs":[{"output_type":"stream","text":"0.9852281360342515\n","name":"stdout"}]},{"metadata":{},"cell_type":"markdown","source":"2\\. Let's split the data by the feature \"Looks_handsome\". What is the entropy $S_1$ of the left group - the one with \"Looks_handsome\". What is the entropy $S_2$ in the opposite group? What is the information gain (IG) if we consider such a split?"},{"metadata":{"trusted":true},"cell_type":"code","source":"# you code here\n# for the feature Looks_handsome, 1 means handsome, 0 means repulsive\n\nleft_group, right_group = [],[]\nfor index,value in df_train['Looks_handsome'].items():\n    if value == 1:\n        left_group.append(y.loc[index])\n    else:\n        right_group.append(y.loc[index])\n\ndef calculate_entropy(group):               #for binary and parameter group is a list\n    length = len(group)\n    num_pos = 0\n    for i in group:\n        if i == 1:\n            num_pos += 1\n    \n    p_pos = num_pos/length\n    entropy = -p_pos * np.log2(p_pos) - (1-p_pos) * np.log2(1-p_pos)\n    \n    return entropy\n\ns1 = calculate_entropy(left_group)\ns2 = calculate_entropy(right_group)\n\nig = s0 - (len(left_group)/size)*s1 - (len(right_group)/size)*s2\n\nprint(s1,s2,ig)\n    ","execution_count":9,"outputs":[{"output_type":"stream","text":"0.8112781244591328 0.9182958340544896 0.12808527889139443\n","name":"stdout"}]},{"metadata":{},"cell_type":"markdown","source":"#### Train a decision tree using sklearn on the training data. You may choose any depth for the tree."},{"metadata":{"trusted":true},"cell_type":"code","source":"# you code here\ntree = DecisionTreeClassifier(max_depth = 5, random_state = 17) \ntree.fit(df_train,y)   \n#train using random parameters","execution_count":10,"outputs":[{"output_type":"execute_result","execution_count":10,"data":{"text/plain":"DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=5,\n            max_features=None, max_leaf_nodes=None,\n            min_impurity_decrease=0.0, min_impurity_split=None,\n            min_samples_leaf=1, min_samples_split=2,\n            min_weight_fraction_leaf=0.0, presort=False, random_state=17,\n            splitter='best')"},"metadata":{}}]},{"metadata":{},"cell_type":"markdown","source":"#### Additional: display the resulting tree using graphviz. You can use pydot or [web-service](https://www.coolutils.com/ru/online/DOT-to-PNG) dot2png."},{"metadata":{"trusted":true},"cell_type":"code","source":"# you code here\ndef tree_graph_to_png(tree, feature_names, png_file_to_save):\n    tree_str = export_graphviz(tree, feature_names=feature_names, \n                                     filled=True, out_file=None)\n    graph = pydotplus.graph_from_dot_data(tree_str)  \n    graph.write_png(png_file_to_save)\n    \ntree_graph_to_png(tree=tree, feature_names = df_train.columns, png_file_to_save = 'assignment3_decisiontree1.png')\n\n# Display in python\n\nplt.figure(figsize = (14, 18))\nplt.imshow(plt.imread('assignment3_decisiontree1.png'))\nplt.axis('off');\nplt.show();","execution_count":11,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 1008x1296 with 1 Axes>","image/png":"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\n"},"metadata":{"needs_background":"light"}}]},{"metadata":{},"cell_type":"markdown","source":"### Part 2. Functions for calculating entropy and information gain."},{"metadata":{},"cell_type":"markdown","source":"Consider the following warm-up example: we have 9 blue balls and 11 yellow balls. Let ball have label **1** if it is blue, **0** otherwise."},{"metadata":{"trusted":true},"cell_type":"code","source":"balls = [1 for i in range(9)] + [0 for i in range(11)]","execution_count":12,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"<img src = 'https://habrastorage.org/webt/mu/vl/mt/muvlmtd2njeqf18trbldenpqvnm.png'>"},{"metadata":{},"cell_type":"markdown","source":"Next split the balls into two groups:"},{"metadata":{},"cell_type":"markdown","source":"<img src='https://habrastorage.org/webt/bd/aq/5w/bdaq5wi3c4feezaexponvin8wmo.png'>"},{"metadata":{"trusted":true},"cell_type":"code","source":"# two groups\nballs_left  = [1 for i in range(8)] + [0 for i in range(5)] # 8 blue and 5 yellow\nballs_right = [1 for i in range(1)] + [0 for i in range(6)] # 1 blue and 6 yellow","execution_count":13,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"#### Implement a function to calculate the Shannon Entropy"},{"metadata":{"trusted":true},"cell_type":"code","source":"#l = [[1,1],[2,2,2],[3,3]]\n#leng = 0\n#for i in l:\n#    leng += len(i)\n#print(leng)","execution_count":14,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"import itertools\ndef entropy(a_list):\n    a_list.sort()\n    b_list = [list(g) for k,g in itertools.groupby(a_list)]\n    \n    total_num = 0\n    p_list = []\n    \n    for i in b_list:\n        total_num += len(i)\n        p_list.append(len(i))\n    \n    sum = 0\n    \n    for j in p_list:\n        sum += -(j/total_num)*np.log2(j/total_num)\n    \n    return sum\n\n    \n    ","execution_count":15,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"Tests"},{"metadata":{"trusted":true},"cell_type":"code","source":"print(entropy(balls)) # 9 blue и 11 yellow\nprint(entropy(balls_left)) # 8 blue и 5 yellow\nprint(entropy(balls_right)) # 1 blue и 6 yellow\nprint(entropy([1,2,3,4,5,6])) # entropy of a fair 6-sided die","execution_count":16,"outputs":[{"output_type":"stream","text":"0.9927744539878083\n0.9612366047228759\n0.5916727785823275\n2.584962500721156\n","name":"stdout"}]},{"metadata":{},"cell_type":"markdown","source":"3\\. What is the entropy of the state given by the list **balls_left**?"},{"metadata":{},"cell_type":"markdown","source":"4\\. What is the entropy of a fair dice? (where we look at a dice as a system with 6 equally probable states)?"},{"metadata":{"trusted":true},"cell_type":"code","source":"# information gain calculation\ndef information_gain(root, left, right):\n    ''' root - initial data, left and right - two partitions of initial data'''\n    \n    s_root = entropy(root)\n    s_left = entropy(left)\n    s_right = entropy(right)\n    \n    info_gain = s_root - (len(left)/len(root))*s_left - (len(right)/len(root))*s_right\n    \n    return info_gain","execution_count":17,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"print(information_gain(balls,balls_left,balls_right))","execution_count":18,"outputs":[{"output_type":"stream","text":"0.16088518841412436\n","name":"stdout"}]},{"metadata":{},"cell_type":"markdown","source":"5\\. What is the information gain from splitting the initial dataset into **balls_left** and **balls_right** ?"},{"metadata":{"trusted":true},"cell_type":"code","source":"def best_feature_to_split(X, y):\n    '''Outputs information gain when splitting on best feature'''\n    \n    # you code here\n    pass","execution_count":19,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"#### Optional:\n- Implement a decision tree building algorithm by calling **best_feature_to_split** recursively\n- Plot the resulting tree"},{"metadata":{},"cell_type":"markdown","source":"### Part 3. The \"Adult\" dataset"},{"metadata":{},"cell_type":"markdown","source":"#### Dataset description:"},{"metadata":{},"cell_type":"markdown","source":"[Dataset](http://archive.ics.uci.edu/ml/machine-learning-databases/adult) UCI Adult (no need to download it, we have a copy in the course repository): classify people using demographical data - whether they earn more than \\$50,000 per year or not."},{"metadata":{},"cell_type":"markdown","source":"Feature descriptions:"},{"metadata":{},"cell_type":"markdown","source":"- **Age** – continuous feature\n- **Workclass** –  continuous feature\n- **fnlwgt** – final weight of object, continuous feature\n- **Education** –  categorical feature\n- **Education_Num** – number of years of education, continuous feature\n- **Martial_Status** –  categorical feature\n- **Occupation** –  categorical feature\n- **Relationship** – categorical feature\n- **Race** – categorical feature\n- **Sex** – categorical feature\n- **Capital_Gain** – continuous feature\n- **Capital_Loss** – continuous feature\n- **Hours_per_week** – continuous feature\n- **Country** – categorical feature"},{"metadata":{},"cell_type":"markdown","source":"**Target** – earnings level, categorical (binary) feature."},{"metadata":{},"cell_type":"markdown","source":"#### Reading train and test data"},{"metadata":{"trusted":true},"cell_type":"code","source":"data_train = pd.read_csv('../input/adult_train.csv')","execution_count":20,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"data_train.tail()","execution_count":21,"outputs":[{"output_type":"execute_result","execution_count":21,"data":{"text/plain":"       Age      Workclass   ...           Country  Target\n32556   27        Private   ...     United-States   <=50K\n32557   40        Private   ...     United-States    >50K\n32558   58        Private   ...     United-States   <=50K\n32559   22        Private   ...     United-States   <=50K\n32560   52   Self-emp-inc   ...     United-States    >50K\n\n[5 rows x 15 columns]","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Age</th>\n      <th>Workclass</th>\n      <th>fnlwgt</th>\n      <th>Education</th>\n      <th>Education_Num</th>\n      <th>Martial_Status</th>\n      <th>Occupation</th>\n      <th>Relationship</th>\n      <th>Race</th>\n      <th>Sex</th>\n      <th>Capital_Gain</th>\n      <th>Capital_Loss</th>\n      <th>Hours_per_week</th>\n      <th>Country</th>\n      <th>Target</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>32556</th>\n      <td>27</td>\n      <td>Private</td>\n      <td>257302</td>\n      <td>Assoc-acdm</td>\n      <td>12</td>\n      <td>Married-civ-spouse</td>\n      <td>Tech-support</td>\n      <td>Wife</td>\n      <td>White</td>\n      <td>Female</td>\n      <td>0</td>\n      <td>0</td>\n      <td>38</td>\n      <td>United-States</td>\n      <td>&lt;=50K</td>\n    </tr>\n    <tr>\n      <th>32557</th>\n      <td>40</td>\n      <td>Private</td>\n      <td>154374</td>\n      <td>HS-grad</td>\n      <td>9</td>\n      <td>Married-civ-spouse</td>\n      <td>Machine-op-inspct</td>\n      <td>Husband</td>\n      <td>White</td>\n      <td>Male</td>\n      <td>0</td>\n      <td>0</td>\n      <td>40</td>\n      <td>United-States</td>\n      <td>&gt;50K</td>\n    </tr>\n    <tr>\n      <th>32558</th>\n      <td>58</td>\n      <td>Private</td>\n      <td>151910</td>\n      <td>HS-grad</td>\n      <td>9</td>\n      <td>Widowed</td>\n      <td>Adm-clerical</td>\n      <td>Unmarried</td>\n      <td>White</td>\n      <td>Female</td>\n      <td>0</td>\n      <td>0</td>\n      <td>40</td>\n      <td>United-States</td>\n      <td>&lt;=50K</td>\n    </tr>\n    <tr>\n      <th>32559</th>\n      <td>22</td>\n      <td>Private</td>\n      <td>201490</td>\n      <td>HS-grad</td>\n      <td>9</td>\n      <td>Never-married</td>\n      <td>Adm-clerical</td>\n      <td>Own-child</td>\n      <td>White</td>\n      <td>Male</td>\n      <td>0</td>\n      <td>0</td>\n      <td>20</td>\n      <td>United-States</td>\n      <td>&lt;=50K</td>\n    </tr>\n    <tr>\n      <th>32560</th>\n      <td>52</td>\n      <td>Self-emp-inc</td>\n      <td>287927</td>\n      <td>HS-grad</td>\n      <td>9</td>\n      <td>Married-civ-spouse</td>\n      <td>Exec-managerial</td>\n      <td>Wife</td>\n      <td>White</td>\n      <td>Female</td>\n      <td>15024</td>\n      <td>0</td>\n      <td>40</td>\n      <td>United-States</td>\n      <td>&gt;50K</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"data_test = pd.read_csv('../input/adult_test.csv')","execution_count":22,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"data_test.tail()","execution_count":23,"outputs":[{"output_type":"execute_result","execution_count":23,"data":{"text/plain":"      Age      Workclass   ...            Country   Target\n16277  39        Private   ...      United-States   <=50K.\n16278  64            NaN   ...      United-States   <=50K.\n16279  38        Private   ...      United-States   <=50K.\n16280  44        Private   ...      United-States   <=50K.\n16281  35   Self-emp-inc   ...      United-States    >50K.\n\n[5 rows x 15 columns]","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Age</th>\n      <th>Workclass</th>\n      <th>fnlwgt</th>\n      <th>Education</th>\n      <th>Education_Num</th>\n      <th>Martial_Status</th>\n      <th>Occupation</th>\n      <th>Relationship</th>\n      <th>Race</th>\n      <th>Sex</th>\n      <th>Capital_Gain</th>\n      <th>Capital_Loss</th>\n      <th>Hours_per_week</th>\n      <th>Country</th>\n      <th>Target</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>16277</th>\n      <td>39</td>\n      <td>Private</td>\n      <td>215419.0</td>\n      <td>Bachelors</td>\n      <td>13.0</td>\n      <td>Divorced</td>\n      <td>Prof-specialty</td>\n      <td>Not-in-family</td>\n      <td>White</td>\n      <td>Female</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>36.0</td>\n      <td>United-States</td>\n      <td>&lt;=50K.</td>\n    </tr>\n    <tr>\n      <th>16278</th>\n      <td>64</td>\n      <td>NaN</td>\n      <td>321403.0</td>\n      <td>HS-grad</td>\n      <td>9.0</td>\n      <td>Widowed</td>\n      <td>NaN</td>\n      <td>Other-relative</td>\n      <td>Black</td>\n      <td>Male</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>40.0</td>\n      <td>United-States</td>\n      <td>&lt;=50K.</td>\n    </tr>\n    <tr>\n      <th>16279</th>\n      <td>38</td>\n      <td>Private</td>\n      <td>374983.0</td>\n      <td>Bachelors</td>\n      <td>13.0</td>\n      <td>Married-civ-spouse</td>\n      <td>Prof-specialty</td>\n      <td>Husband</td>\n      <td>White</td>\n      <td>Male</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>50.0</td>\n      <td>United-States</td>\n      <td>&lt;=50K.</td>\n    </tr>\n    <tr>\n      <th>16280</th>\n      <td>44</td>\n      <td>Private</td>\n      <td>83891.0</td>\n      <td>Bachelors</td>\n      <td>13.0</td>\n      <td>Divorced</td>\n      <td>Adm-clerical</td>\n      <td>Own-child</td>\n      <td>Asian-Pac-Islander</td>\n      <td>Male</td>\n      <td>5455.0</td>\n      <td>0.0</td>\n      <td>40.0</td>\n      <td>United-States</td>\n      <td>&lt;=50K.</td>\n    </tr>\n    <tr>\n      <th>16281</th>\n      <td>35</td>\n      <td>Self-emp-inc</td>\n      <td>182148.0</td>\n      <td>Bachelors</td>\n      <td>13.0</td>\n      <td>Married-civ-spouse</td>\n      <td>Exec-managerial</td>\n      <td>Husband</td>\n      <td>White</td>\n      <td>Male</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>60.0</td>\n      <td>United-States</td>\n      <td>&gt;50K.</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"# necessary to remove rows with incorrect labels in test dataset\ndata_test = data_test[(data_test['Target'] == ' >50K.') | (data_test['Target']==' <=50K.')]\n\n# encode target variable as integer\ndata_train.loc[data_train['Target']==' <=50K', 'Target'] = 0\ndata_train.loc[data_train['Target']==' >50K', 'Target'] = 1\n\ndata_test.loc[data_test['Target']==' <=50K.', 'Target'] = 0\ndata_test.loc[data_test['Target']==' >50K.', 'Target'] = 1","execution_count":24,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"#### Primary data analysis"},{"metadata":{"trusted":true},"cell_type":"code","source":"data_test.describe(include='all').T","execution_count":25,"outputs":[{"output_type":"execute_result","execution_count":25,"data":{"text/plain":"                count unique     ...         75%         max\nAge             16281     73     ...         NaN         NaN\nWorkclass       15318      8     ...         NaN         NaN\nfnlwgt          16281    NaN     ...      238384  1.4904e+06\nEducation       16281     16     ...         NaN         NaN\nEducation_Num   16281    NaN     ...          12          16\nMartial_Status  16281      7     ...         NaN         NaN\nOccupation      15315     14     ...         NaN         NaN\nRelationship    16281      6     ...         NaN         NaN\nRace            16281      5     ...         NaN         NaN\nSex             16281      2     ...         NaN         NaN\nCapital_Gain    16281    NaN     ...           0       99999\nCapital_Loss    16281    NaN     ...           0        3770\nHours_per_week  16281    NaN     ...          45          99\nCountry         16007     40     ...         NaN         NaN\nTarget          16281    NaN     ...           0           1\n\n[15 rows x 11 columns]","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>count</th>\n      <th>unique</th>\n      <th>top</th>\n      <th>freq</th>\n      <th>mean</th>\n      <th>std</th>\n      <th>min</th>\n      <th>25%</th>\n      <th>50%</th>\n      <th>75%</th>\n      <th>max</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>Age</th>\n      <td>16281</td>\n      <td>73</td>\n      <td>35</td>\n      <td>461</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>Workclass</th>\n      <td>15318</td>\n      <td>8</td>\n      <td>Private</td>\n      <td>11210</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>fnlwgt</th>\n      <td>16281</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>189436</td>\n      <td>105715</td>\n      <td>13492</td>\n      <td>116736</td>\n      <td>177831</td>\n      <td>238384</td>\n      <td>1.4904e+06</td>\n    </tr>\n    <tr>\n      <th>Education</th>\n      <td>16281</td>\n      <td>16</td>\n      <td>HS-grad</td>\n      <td>5283</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>Education_Num</th>\n      <td>16281</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>10.0729</td>\n      <td>2.56755</td>\n      <td>1</td>\n      <td>9</td>\n      <td>10</td>\n      <td>12</td>\n      <td>16</td>\n    </tr>\n    <tr>\n      <th>Martial_Status</th>\n      <td>16281</td>\n      <td>7</td>\n      <td>Married-civ-spouse</td>\n      <td>7403</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>Occupation</th>\n      <td>15315</td>\n      <td>14</td>\n      <td>Prof-specialty</td>\n      <td>2032</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>Relationship</th>\n      <td>16281</td>\n      <td>6</td>\n      <td>Husband</td>\n      <td>6523</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>Race</th>\n      <td>16281</td>\n      <td>5</td>\n      <td>White</td>\n      <td>13946</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>Sex</th>\n      <td>16281</td>\n      <td>2</td>\n      <td>Male</td>\n      <td>10860</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>Capital_Gain</th>\n      <td>16281</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>1081.91</td>\n      <td>7583.94</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>99999</td>\n    </tr>\n    <tr>\n      <th>Capital_Loss</th>\n      <td>16281</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>87.8993</td>\n      <td>403.105</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>3770</td>\n    </tr>\n    <tr>\n      <th>Hours_per_week</th>\n      <td>16281</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>40.3922</td>\n      <td>12.4793</td>\n      <td>1</td>\n      <td>40</td>\n      <td>40</td>\n      <td>45</td>\n      <td>99</td>\n    </tr>\n    <tr>\n      <th>Country</th>\n      <td>16007</td>\n      <td>40</td>\n      <td>United-States</td>\n      <td>14662</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>Target</th>\n      <td>16281</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.236226</td>\n      <td>0.424776</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"data_train['Target'].value_counts()","execution_count":26,"outputs":[{"output_type":"execute_result","execution_count":26,"data":{"text/plain":"0    24720\n1     7841\nName: Target, dtype: int64"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"fig = plt.figure(figsize=(25, 15))\ncols = 5\nrows = np.ceil(float(data_train.shape[1]) / cols)\nfor i, column in enumerate(data_train.columns):\n    ax = fig.add_subplot(rows, cols, i + 1)\n    ax.set_title(column)\n    if data_train.dtypes[column] == np.object:\n        data_train[column].value_counts().plot(kind=\"bar\", axes=ax)\n    else:\n        data_train[column].hist(axes=ax)\n        plt.xticks(rotation=\"vertical\")\nplt.subplots_adjust(hspace=0.7, wspace=0.2)","execution_count":27,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 1800x1080 with 15 Axes>","image/png":"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\n"},"metadata":{"needs_background":"light"}}]},{"metadata":{},"cell_type":"markdown","source":"#### Checking data types"},{"metadata":{"trusted":true},"cell_type":"code","source":"data_train.dtypes","execution_count":28,"outputs":[{"output_type":"execute_result","execution_count":28,"data":{"text/plain":"Age                int64\nWorkclass         object\nfnlwgt             int64\nEducation         object\nEducation_Num      int64\nMartial_Status    object\nOccupation        object\nRelationship      object\nRace              object\nSex               object\nCapital_Gain       int64\nCapital_Loss       int64\nHours_per_week     int64\nCountry           object\nTarget             int64\ndtype: object"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"data_test.dtypes","execution_count":29,"outputs":[{"output_type":"execute_result","execution_count":29,"data":{"text/plain":"Age                object\nWorkclass          object\nfnlwgt            float64\nEducation          object\nEducation_Num     float64\nMartial_Status     object\nOccupation         object\nRelationship       object\nRace               object\nSex                object\nCapital_Gain      float64\nCapital_Loss      float64\nHours_per_week    float64\nCountry            object\nTarget              int64\ndtype: object"},"metadata":{}}]},{"metadata":{},"cell_type":"markdown","source":"As we see, in the test data, age is treated as type **object**. We need to fix this."},{"metadata":{"trusted":true},"cell_type":"code","source":"data_test['Age'] = data_test['Age'].astype(int)","execution_count":30,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"Also we'll cast all **float** features to **int** type to keep types consistent between our train and test data."},{"metadata":{"trusted":true},"cell_type":"code","source":"data_test['fnlwgt'] = data_test['fnlwgt'].astype(int)\ndata_test['Education_Num'] = data_test['Education_Num'].astype(int)\ndata_test['Capital_Gain'] = data_test['Capital_Gain'].astype(int)\ndata_test['Capital_Loss'] = data_test['Capital_Loss'].astype(int)\ndata_test['Hours_per_week'] = data_test['Hours_per_week'].astype(int)","execution_count":31,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"#### Fill in missing data for continuous features with their median values, for categorical features with their mode."},{"metadata":{"trusted":true},"cell_type":"code","source":"# choose categorical and continuous features from data\n\ncategorical_columns = [c for c in data_train.columns \n                       if data_train[c].dtype.name == 'object']\nnumerical_columns = [c for c in data_train.columns \n                     if data_train[c].dtype.name != 'object']\n\nprint('categorical_columns:', categorical_columns)\nprint('numerical_columns:', numerical_columns)","execution_count":32,"outputs":[{"output_type":"stream","text":"categorical_columns: ['Workclass', 'Education', 'Martial_Status', 'Occupation', 'Relationship', 'Race', 'Sex', 'Country']\nnumerical_columns: ['Age', 'fnlwgt', 'Education_Num', 'Capital_Gain', 'Capital_Loss', 'Hours_per_week', 'Target']\n","name":"stdout"}]},{"metadata":{"trusted":true},"cell_type":"code","source":"# we see some missing values\ndata_train.info()","execution_count":33,"outputs":[{"output_type":"stream","text":"<class 'pandas.core.frame.DataFrame'>\nRangeIndex: 32561 entries, 0 to 32560\nData columns (total 15 columns):\nAge               32561 non-null int64\nWorkclass         30725 non-null object\nfnlwgt            32561 non-null int64\nEducation         32561 non-null object\nEducation_Num     32561 non-null int64\nMartial_Status    32561 non-null object\nOccupation        30718 non-null object\nRelationship      32561 non-null object\nRace              32561 non-null object\nSex               32561 non-null object\nCapital_Gain      32561 non-null int64\nCapital_Loss      32561 non-null int64\nHours_per_week    32561 non-null int64\nCountry           31978 non-null object\nTarget            32561 non-null int64\ndtypes: int64(7), object(8)\nmemory usage: 3.7+ MB\n","name":"stdout"}]},{"metadata":{"trusted":true},"cell_type":"code","source":"# fill missing data\n\nfor c in categorical_columns:\n    data_train[c].fillna(data_train[c].mode()[0], inplace=True)\n    data_test[c].fillna(data_train[c].mode()[0], inplace=True)\n    \nfor c in numerical_columns:\n    data_train[c].fillna(data_train[c].median(), inplace=True)\n    data_test[c].fillna(data_train[c].median(), inplace=True)","execution_count":34,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"# no more missing values\ndata_train.info()","execution_count":35,"outputs":[{"output_type":"stream","text":"<class 'pandas.core.frame.DataFrame'>\nRangeIndex: 32561 entries, 0 to 32560\nData columns (total 15 columns):\nAge               32561 non-null int64\nWorkclass         32561 non-null object\nfnlwgt            32561 non-null int64\nEducation         32561 non-null object\nEducation_Num     32561 non-null int64\nMartial_Status    32561 non-null object\nOccupation        32561 non-null object\nRelationship      32561 non-null object\nRace              32561 non-null object\nSex               32561 non-null object\nCapital_Gain      32561 non-null int64\nCapital_Loss      32561 non-null int64\nHours_per_week    32561 non-null int64\nCountry           32561 non-null object\nTarget            32561 non-null int64\ndtypes: int64(7), object(8)\nmemory usage: 3.7+ MB\n","name":"stdout"}]},{"metadata":{},"cell_type":"markdown","source":"We'll dummy code some categorical features: **Workclass**, **Education**, **Martial_Status**, **Occupation**, **Relationship**, **Race**, **Sex**, **Country**. It can be done via pandas method **get_dummies**"},{"metadata":{"trusted":true},"cell_type":"code","source":"data_train = pd.concat([data_train[numerical_columns],\n    pd.get_dummies(data_train[categorical_columns])], axis=1)\n\ndata_test = pd.concat([data_test[numerical_columns],\n    pd.get_dummies(data_test[categorical_columns])], axis=1)","execution_count":36,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"set(data_train.columns) - set(data_test.columns)","execution_count":37,"outputs":[{"output_type":"execute_result","execution_count":37,"data":{"text/plain":"{'Country_ Holand-Netherlands'}"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"data_train.shape, data_test.shape","execution_count":38,"outputs":[{"output_type":"execute_result","execution_count":38,"data":{"text/plain":"((32561, 106), (16281, 105))"},"metadata":{}}]},{"metadata":{},"cell_type":"markdown","source":"#### There is no Holland in the test data. Create new zero-valued feature."},{"metadata":{"trusted":true},"cell_type":"code","source":"data_test['Country_ Holand-Netherlands'] = 0","execution_count":39,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"set(data_train.columns) - set(data_test.columns)","execution_count":40,"outputs":[{"output_type":"execute_result","execution_count":40,"data":{"text/plain":"set()"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"data_train.head(2)","execution_count":41,"outputs":[{"output_type":"execute_result","execution_count":41,"data":{"text/plain":"   Age  fnlwgt         ...           Country_ Vietnam  Country_ Yugoslavia\n0   39   77516         ...                          0                    0\n1   50   83311         ...                          0                    0\n\n[2 rows x 106 columns]","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Age</th>\n      <th>fnlwgt</th>\n      <th>Education_Num</th>\n      <th>Capital_Gain</th>\n      <th>Capital_Loss</th>\n      <th>Hours_per_week</th>\n      <th>Target</th>\n      <th>Workclass_ Federal-gov</th>\n      <th>Workclass_ Local-gov</th>\n      <th>Workclass_ Never-worked</th>\n      <th>Workclass_ Private</th>\n      <th>Workclass_ Self-emp-inc</th>\n      <th>Workclass_ Self-emp-not-inc</th>\n      <th>Workclass_ State-gov</th>\n      <th>Workclass_ Without-pay</th>\n      <th>Education_ 10th</th>\n      <th>Education_ 11th</th>\n      <th>Education_ 12th</th>\n      <th>Education_ 1st-4th</th>\n      <th>Education_ 5th-6th</th>\n      <th>Education_ 7th-8th</th>\n      <th>Education_ 9th</th>\n      <th>Education_ Assoc-acdm</th>\n      <th>Education_ Assoc-voc</th>\n      <th>Education_ Bachelors</th>\n      <th>Education_ Doctorate</th>\n      <th>Education_ HS-grad</th>\n      <th>Education_ Masters</th>\n      <th>Education_ Preschool</th>\n      <th>Education_ Prof-school</th>\n      <th>Education_ Some-college</th>\n      <th>Martial_Status_ Divorced</th>\n      <th>Martial_Status_ Married-AF-spouse</th>\n      <th>Martial_Status_ Married-civ-spouse</th>\n      <th>Martial_Status_ Married-spouse-absent</th>\n      <th>Martial_Status_ Never-married</th>\n      <th>Martial_Status_ Separated</th>\n      <th>Martial_Status_ Widowed</th>\n      <th>Occupation_ Adm-clerical</th>\n      <th>Occupation_ Armed-Forces</th>\n      <th>...</th>\n      <th>Country_ Canada</th>\n      <th>Country_ China</th>\n      <th>Country_ Columbia</th>\n      <th>Country_ Cuba</th>\n      <th>Country_ Dominican-Republic</th>\n      <th>Country_ Ecuador</th>\n      <th>Country_ El-Salvador</th>\n      <th>Country_ England</th>\n      <th>Country_ France</th>\n      <th>Country_ Germany</th>\n      <th>Country_ Greece</th>\n      <th>Country_ Guatemala</th>\n      <th>Country_ Haiti</th>\n      <th>Country_ Holand-Netherlands</th>\n      <th>Country_ Honduras</th>\n      <th>Country_ Hong</th>\n      <th>Country_ Hungary</th>\n      <th>Country_ India</th>\n      <th>Country_ Iran</th>\n      <th>Country_ Ireland</th>\n      <th>Country_ Italy</th>\n      <th>Country_ Jamaica</th>\n      <th>Country_ Japan</th>\n      <th>Country_ Laos</th>\n      <th>Country_ Mexico</th>\n      <th>Country_ Nicaragua</th>\n      <th>Country_ Outlying-US(Guam-USVI-etc)</th>\n      <th>Country_ Peru</th>\n      <th>Country_ Philippines</th>\n      <th>Country_ Poland</th>\n      <th>Country_ Portugal</th>\n      <th>Country_ Puerto-Rico</th>\n      <th>Country_ Scotland</th>\n      <th>Country_ South</th>\n      <th>Country_ Taiwan</th>\n      <th>Country_ Thailand</th>\n      <th>Country_ Trinadad&amp;Tobago</th>\n      <th>Country_ United-States</th>\n      <th>Country_ Vietnam</th>\n      <th>Country_ Yugoslavia</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>39</td>\n      <td>77516</td>\n      <td>13</td>\n      <td>2174</td>\n      <td>0</td>\n      <td>40</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>50</td>\n      <td>83311</td>\n      <td>13</td>\n      <td>0</td>\n      <td>0</td>\n      <td>13</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"data_test.head(2)","execution_count":42,"outputs":[{"output_type":"execute_result","execution_count":42,"data":{"text/plain":"   Age             ...               Country_ Holand-Netherlands\n1   25             ...                                         0\n2   38             ...                                         0\n\n[2 rows x 106 columns]","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Age</th>\n      <th>fnlwgt</th>\n      <th>Education_Num</th>\n      <th>Capital_Gain</th>\n      <th>Capital_Loss</th>\n      <th>Hours_per_week</th>\n      <th>Target</th>\n      <th>Workclass_ Federal-gov</th>\n      <th>Workclass_ Local-gov</th>\n      <th>Workclass_ Never-worked</th>\n      <th>Workclass_ Private</th>\n      <th>Workclass_ Self-emp-inc</th>\n      <th>Workclass_ Self-emp-not-inc</th>\n      <th>Workclass_ State-gov</th>\n      <th>Workclass_ Without-pay</th>\n      <th>Education_ 10th</th>\n      <th>Education_ 11th</th>\n      <th>Education_ 12th</th>\n      <th>Education_ 1st-4th</th>\n      <th>Education_ 5th-6th</th>\n      <th>Education_ 7th-8th</th>\n      <th>Education_ 9th</th>\n      <th>Education_ Assoc-acdm</th>\n      <th>Education_ Assoc-voc</th>\n      <th>Education_ Bachelors</th>\n      <th>Education_ Doctorate</th>\n      <th>Education_ HS-grad</th>\n      <th>Education_ Masters</th>\n      <th>Education_ Preschool</th>\n      <th>Education_ Prof-school</th>\n      <th>Education_ Some-college</th>\n      <th>Martial_Status_ Divorced</th>\n      <th>Martial_Status_ Married-AF-spouse</th>\n      <th>Martial_Status_ Married-civ-spouse</th>\n      <th>Martial_Status_ Married-spouse-absent</th>\n      <th>Martial_Status_ Never-married</th>\n      <th>Martial_Status_ Separated</th>\n      <th>Martial_Status_ Widowed</th>\n      <th>Occupation_ Adm-clerical</th>\n      <th>Occupation_ Armed-Forces</th>\n      <th>...</th>\n      <th>Country_ Canada</th>\n      <th>Country_ China</th>\n      <th>Country_ Columbia</th>\n      <th>Country_ Cuba</th>\n      <th>Country_ Dominican-Republic</th>\n      <th>Country_ Ecuador</th>\n      <th>Country_ El-Salvador</th>\n      <th>Country_ England</th>\n      <th>Country_ France</th>\n      <th>Country_ Germany</th>\n      <th>Country_ Greece</th>\n      <th>Country_ Guatemala</th>\n      <th>Country_ Haiti</th>\n      <th>Country_ Honduras</th>\n      <th>Country_ Hong</th>\n      <th>Country_ Hungary</th>\n      <th>Country_ India</th>\n      <th>Country_ Iran</th>\n      <th>Country_ Ireland</th>\n      <th>Country_ Italy</th>\n      <th>Country_ Jamaica</th>\n      <th>Country_ Japan</th>\n      <th>Country_ Laos</th>\n      <th>Country_ Mexico</th>\n      <th>Country_ Nicaragua</th>\n      <th>Country_ Outlying-US(Guam-USVI-etc)</th>\n      <th>Country_ Peru</th>\n      <th>Country_ Philippines</th>\n      <th>Country_ Poland</th>\n      <th>Country_ Portugal</th>\n      <th>Country_ Puerto-Rico</th>\n      <th>Country_ Scotland</th>\n      <th>Country_ South</th>\n      <th>Country_ Taiwan</th>\n      <th>Country_ Thailand</th>\n      <th>Country_ Trinadad&amp;Tobago</th>\n      <th>Country_ United-States</th>\n      <th>Country_ Vietnam</th>\n      <th>Country_ Yugoslavia</th>\n      <th>Country_ Holand-Netherlands</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>1</th>\n      <td>25</td>\n      <td>226802</td>\n      <td>7</td>\n      <td>0</td>\n      <td>0</td>\n      <td>40</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>38</td>\n      <td>89814</td>\n      <td>9</td>\n      <td>0</td>\n      <td>0</td>\n      <td>50</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"X_train = data_train.drop(['Target'], axis=1)\ny_train = data_train['Target']\n\nX_test = data_test.drop(['Target'], axis=1)\ny_test = data_test['Target']","execution_count":43,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"### 3.1 Decision tree without parameter tuning"},{"metadata":{},"cell_type":"markdown","source":"Train a decision tree **(DecisionTreeClassifier)** with a maximum depth of 3, and evaluate the accuracy metric on the test data. Use parameter **random_state = 17** for results reproducibility."},{"metadata":{"trusted":true},"cell_type":"code","source":"# you code here\n# tree = \n# tree.fit\n\ntree_classifier = DecisionTreeClassifier(max_depth = 3, random_state = 17)\ntree_classifier.fit(X_train, y_train)","execution_count":53,"outputs":[{"output_type":"execute_result","execution_count":53,"data":{"text/plain":"DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=3,\n            max_features=None, max_leaf_nodes=None,\n            min_impurity_decrease=0.0, min_impurity_split=None,\n            min_samples_leaf=1, min_samples_split=2,\n            min_weight_fraction_leaf=0.0, presort=False, random_state=17,\n            splitter='best')"},"metadata":{}}]},{"metadata":{},"cell_type":"markdown","source":"Make a prediction with the trained model on the test data."},{"metadata":{"trusted":true},"cell_type":"code","source":"# you code here\n# tree_predictions = tree.predict \ntree_classifier_predictions = tree_classifier.predict(X_test)","execution_count":54,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"# you code here\n# accuracy_score \naccuracy_score(y_test, tree_classifier_predictions)","execution_count":55,"outputs":[{"output_type":"execute_result","execution_count":55,"data":{"text/plain":"0.8447884036607088"},"metadata":{}}]},{"metadata":{},"cell_type":"markdown","source":"6\\. What is the test set accuracy of a decision tree with maximum tree depth of 3 and **random_state = 17**?"},{"metadata":{},"cell_type":"markdown","source":"### 3.2 Decision tree with parameter tuning"},{"metadata":{},"cell_type":"markdown","source":"Train a decision tree **(DecisionTreeClassifier, random_state = 17).** Find the optimal maximum depth using 5-fold cross-validation **(GridSearchCV)**."},{"metadata":{"trusted":true},"cell_type":"code","source":"tree_params = {'max_depth': range(2,11)}\n\nlocally_best_tree = GridSearchCV(tree_classifier, tree_params, cv = 5, n_jobs = -1, verbose = True) # you code here                     \n\nlocally_best_tree.fit(X_train, y_train) # you code here ","execution_count":56,"outputs":[{"output_type":"stream","text":"Fitting 5 folds for each of 9 candidates, totalling 45 fits\n","name":"stdout"},{"output_type":"stream","text":"[Parallel(n_jobs=-1)]: Done  45 out of  45 | elapsed:    3.3s finished\n","name":"stderr"},{"output_type":"execute_result","execution_count":56,"data":{"text/plain":"GridSearchCV(cv=5, error_score='raise',\n       estimator=DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=3,\n            max_features=None, max_leaf_nodes=None,\n            min_impurity_decrease=0.0, min_impurity_split=None,\n            min_samples_leaf=1, min_samples_split=2,\n            min_weight_fraction_leaf=0.0, presort=False, random_state=17,\n            splitter='best'),\n       fit_params=None, iid=True, n_jobs=-1,\n       param_grid={'max_depth': range(2, 11)}, pre_dispatch='2*n_jobs',\n       refit=True, return_train_score='warn', scoring=None, verbose=True)"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"locally_best_tree.best_params_","execution_count":57,"outputs":[{"output_type":"execute_result","execution_count":57,"data":{"text/plain":"{'max_depth': 9}"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"locally_best_tree.best_score_","execution_count":58,"outputs":[{"output_type":"execute_result","execution_count":58,"data":{"text/plain":"0.8565768864592611"},"metadata":{}}]},{"metadata":{},"cell_type":"markdown","source":"Train a decision tree with maximum depth of 9 (it is the best **max_depth** in my case), and compute the test set accuracy. Use parameter **random_state = 17** for reproducibility."},{"metadata":{"trusted":true},"cell_type":"code","source":"# you code here \n# tuned_tree = \n# tuned_tree.fit \n# tuned_tree_predictions = tuned_tree.predict\n# accuracy_score\n\ntuned_tree = DecisionTreeClassifier(max_depth = 9, random_state = 17)\ntuned_tree.fit(X_train,y_train)\ntuned_tree_predictions = tuned_tree.predict(X_test)\naccuracy_score(y_test, tuned_tree_predictions)","execution_count":59,"outputs":[{"output_type":"execute_result","execution_count":59,"data":{"text/plain":"0.847798046803022"},"metadata":{}}]},{"metadata":{},"cell_type":"markdown","source":"7\\. What is the test set accuracy of a decision tree with maximum depth of 9 and **random_state = 17**?"},{"metadata":{},"cell_type":"markdown","source":"### 3.3 (Optional) Random forest without parameter tuning"},{"metadata":{},"cell_type":"markdown","source":"Let's take a sneak peek of upcoming lectures and try to use a random forest for our task. For now, you can imagine a random forest as a bunch of decision trees, trained on slightly different subsets of the training data."},{"metadata":{},"cell_type":"markdown","source":"Train a random forest **(RandomForestClassifier)**. Set the number of trees to 100 and use **random_state = 17**."},{"metadata":{"trusted":true},"cell_type":"code","source":"# you code here \n# rf = \n# rf.fit # you code here ","execution_count":49,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"Make predictions for the test data and assess accuracy."},{"metadata":{"trusted":true},"cell_type":"code","source":"# you code here ","execution_count":50,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"### 3.4 (Optional) Random forest with parameter tuning"},{"metadata":{},"cell_type":"markdown","source":"Train a random forest **(RandomForestClassifier)**. Tune the maximum depth and maximum number of features for each tree using **GridSearchCV**. "},{"metadata":{"trusted":true},"cell_type":"code","source":"# forest_params = {'max_depth': range(10, 21),\n#                 'max_features': range(5, 105, 20)}\n\n# locally_best_forest = GridSearchCV # you code here \n\n# locally_best_forest.fit # you code here ","execution_count":51,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"Make predictions for the test data and assess accuracy."},{"metadata":{"trusted":true},"cell_type":"code","source":"# you code here ","execution_count":52,"outputs":[]}],"metadata":{"kernelspec":{"name":"python3","display_name":"Python 3","language":"python"},"language_info":{"name":"python","version":"3.6.6","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"}},"nbformat":4,"nbformat_minor":4}