narrow_search.yaml

---
Lasso: 
    alpha: [0.01, 1, 10, 20, 30] #np.arange(0.01, 30.01, 1)

ElasticNet: 
    alpha: [0.01, 1, 10, 20, 30] #np.arange(10, 200, 10)

RandomForest:
    parameters: 
        n_estimators: [25, 50, 75, 100] #[int(x) for x in np.linspace(start = 30, stop = 200, num = 10)] # Number of trees in random forest
        max_features: ['auto', 'sqrt'] # Number of features to consider at every split
        max_depth: [0, 20, 40, 60, 80, 100] #[int(x) for x in np.linspace(10, 110, num = 11)] # Maximum number of levels in tree
        min_samples_split: [2, 3, 4, 5, 6, 7, 8, 9, 10] # Minimum number of samples required to split a node
        min_samples_leaf: [1, 2, 3, 4]  # Minimum number of samples required at each leaf node
        bootstrap: [True, False] # Method of selecting samples for training each tree
    n_iter: 5
    
AdaBoost: 
    parameters:
        n_estimators: [25, 50, 75, 100] #[int(x) for x in np.linspace(start = 20, stop = 100, num = 5)] # Number of decision trees as weak learner
        learning_rate: [0.01, 0.1, 10]
        loss: ["linear", 'square', 'exponential'] 
    n_iter: 5
    
TPOT: 
    generations: 3
    population_size: 50
    verbosity: 2
    max_time_mins: 20
    n_jobs: 2