scikit learn - AshokBhat/ml GitHub Wiki
About
- Python ML Library
- Simple and efficient tools for predictive data analysis
- Accessible to everybody, and reusable in various contexts
- Built on NumPy, SciPy, and matplotlib
- Open source, commercially usable - BSD license
Features
Classification
- Identifying which category an object belongs to.
- Applications: Spam detection, image recognition.
- Algorithms: SVM, nearest neighbors, random forest, and more...
Regression
- Predicting a continuous-valued attribute associated with an object.
- Applications: Drug response, Stock prices.
- Algorithms: SVR, nearest neighbors, random forest, and more...
Clustering
- Automatic grouping of similar objects into sets.
- Applications: Customer segmentation, Grouping experiment outcomes
- Algorithms: k-Means, spectral clustering, mean-shift, and more...
Dimensionality reduction
- Reducing the number of random variables to consider.
- Applications: Visualization, Increased efficiency
- Algorithms: PCA, feature selection, non-negative matrix factorization, and more...
Model selection
- Comparing, validating and choosing parameters and models.
- Applications: Improved accuracy via parameter tuning
- Algorithms: grid search, cross validation, metrics, and more...
Preprocessing
- Feature extraction and normalization.
- Applications: Transforming input data such as text for use with machine learning algorithms.
- Algorithms: preprocessing, feature extraction, and more...