# 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