Skip to content

Latest commit

 

History

History
60 lines (50 loc) · 1.23 KB

README.md

File metadata and controls

60 lines (50 loc) · 1.23 KB
Raw

VanillaML

VanillaML contains a collection of popular machine learning algorithms in Python. The implementations follow scikit-learn's API conventions and are made to be as simple as possible.

Demos


KMeans clustering.


Fitting the curve x*sin(x) using Gradient Boosted Trees.

Requirements

  • Python 2.7
  • Numpy and matplotlib which can be installed via:
$ sudo pip install -r requirements.txt

Learning algorithms

1. Supervised

  • Classification

    • Nearest Neighbors
    • Naive Bayes
    • Perceptron
    • Linear Support Vector Machines
    • Logistic Regression
    • Maximum Entropy
    • Decision Trees
    • Random Forest
    • Feed-forward Neural Network
    • AdaBoost

  • Regression

    • Nearest Neighbors
    • Linear Regression
    • Decision Trees
    • Random Forest
    • Feed-forward Neural Network
    • Gradient Boosting

  • Ranking

    • RankNet
    • Linear RankSVM

2. Unsupervised

  • Clustering
    • K-Means
  • Decomposition
    • PCA

License

BSD