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Model Api

fit!

fit!(model, X) ; fit!(model, X, y)

Trains model on the input data X and y (for supervised learning) or on just X (for unsupervised learning). The model object is always returned, allowing code like classifier = fit!(LogisticRegression(), X, y)

partial_fit!

partial_fit!(model, X) ; partial_fit!(model, X, y)

Incrementally trains model on the new data X and y. For instance, this might perform a stochastic gradient descent step.

predict

predict(model, X) returns the predicted class of each row in X (for classifiers) or the predicted value (for regressors).

predict_proba

predict_proba(model, X) returns an (N, C) matrix containing the probability that the nth sample belongs to the cth class. Call get_classes(model) to get the ordering of the classes.

predictlogproba

predict_log_proba(model, X) is equivalent to log(predict_proba(model, X)) but can be more accurate (for small probabilities) and faster (avoiding the exponential).

transform

For unsupervised learning models and for preprocessing, transform(model, X) applies the transformation from model to X, and returns a similar array (same number of rows, possibly different number of columns).

get_components

For unsupervised learning models, get_components(model) returns the matrix of the latent space, in (ncomponents, nfeatures) form. For matrix factorization methods, this corresponds to the principal components or latent vectors.

fit_transform!

fit_transform!(model, X) is equivalent to transform(fit!(model, X), X) but can sometimes be more efficient.

fit_predict!

fit_predict!(model, X) is equivalent to predict(fit!(model, X), X) but can sometimes be more efficient.

inverse_transform

inverse_transform(model, X) applies the inverse of the model transformation.

score_samples

For probabilistic models, score_samples(model, X) evaluates the density model on X.

score

score(model, X) and score(model, X, y) assign a score to how likely X or y|X is given the learned model parameters. The higher this score is, the better the model. This is used for cross-validation.

decision_function

decision_function(model, X) returns the distance of the samples to the decision boundary.

Model Internals

  • clone(model) returns a new object of the same type as model, with the same hyperparameters, but unfit.
  • set_params!(model, param1=value1, param2=value2, ...) changes the model hyperparameters.
  • get_params(model) returns all the model hyperparameters that can be changed with set_params!
  • is_classifier(model) is true if model is a classifier.
  • get_feature_names(model) returns the name of the output features
  • get_classes(model) returns the label of each class