sklearn
Partial port of scikit-learn to go
Examples
cluster
datasets
LoadIris LoadBreastCancer LoadDiabetes LoadBoston LoadExamScore LoadMicroChipTest LoadMnist LoadMnistWeights MakeRegression MakeBlobs
interpolate
linear_model
LinearRegression BayesianRidge MultiTaskElasticNet MultiTaskLasso ElasticNet Lasso LassoPath LogisticRegression Ridge
metrics
AccuracyScore ConfusionMatrix PrecisionScore RecallScore F1Score FBetaScore PrecisionRecallFScoreSupport ROCCurve AUC ROCAUCScore PrecisionRecallCurve AveragePrecisionScore R2Score
model_selection
neighbors
KNeighborsClassifier MinkowskiDistance EuclideanDistance KDTree NearestCentroid KNeighborsRegressor NearestNeighbors NearestNeighbors.KNeighborsGraph NearestNeighbors.Tree
neural_network
pipeline
preprocessing
MinMaxScaler StandardScaler RobustScaler AddDummyFeature OneHotEncoder Shuffler MaxAbsScaler Binarizer Normalizer Scale KernelCenterer FunctionTransformer Imputer LabelBinarizer MultiLabelBinarizer LabelEncoder PCA
svm
This is
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a personal project to get a deeper understanding of how all of this magic works
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a recent work still in progress, subject to refactoring, so interfaces may change, especially args to NewXXX
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linted with
gofmt, golint, go vetrevive -
unit tested but coverage should reach 90%
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underdocumented but scikit-learn doc is your friend
Many thanks to gonum and scikit-learn authors and contributors
PRs are welcome