Very partial port of scikit-learn to go
for now, ported only some estimators including
- LinearRegression
- LogisticRegression
- bayesian ridge regression
- MLPRegressor
- MLPClassifier
You'll also find
- some metrics MeanSquaredError,MeanAbsoluteError,R2Score,AccuracyScore, ...
- some preprocessing MinMaxScaler,StandardScaler,OneHotEncoder,PolynomialFeatures
- Pipeline and MakePipeline
- some interpolation stuff like in scipy.interpolate: interp1d,interp2d,CubicSpline
- all estimators can use following
- solvers: sgd,adagrad,rmsprop,adadelta,adam + all gonum/optimize methods
- loss functions: square,cross-entropy
- activation functions: identity,logistic,tanh,relu
All of this is
- a personal project to get a deeper understanding of how all of this magic works
- a recent work still in progress, subject to refactoring, so interfaces may change, especially args to NewXXX
- processed with gofmt, golint, go vet
- unit tested but coverage should reach 90%
- underdocumented but python sklearn documentation should be sufficient
Many thanks to gonum and scikit-learn contributors
PRs are welcome
Best regards