Baseline classifier and regressor

fast_automl.baseline.BaselineClassifier

Predicts the most frequent class.

Attributes:	classes_ : array-like of shape (n_classes,) A list of class weights known to the classifier. counts_ : array-like of shape (n_classes,) Normalized frequency of each class in the training data. dominant_class_ : int Class which appears most frequently in the training data.

Examples

from fast_automl.baseline import BaselineClassifier

from sklearn.datasets import load_digits
from sklearn.model_selection import train_test_split

X, y = load_digits(return_X_y=True)
X_train, X_test, y_train, y_test = train_test_split(X, y, stratify=y)
clf = BaselineClassifier().fit(X_train, y_train)
clf.score(X_test, y_test)

Methods

fit(self, X, y, sample_weight=None) [source]

Fit the model.

Parameters:	X : array-like of shape (n_samples, n_features) Training data. y : array-like of shape (n_samples,) Target values. sample_weight, array-like of shape (n_samples,), default=Noone : Individual weights for each sample.
Returns:	self :

predict(self, X) [source]

Predict class labels for samples in X.

Parameters:	X : array-like of shape (n_samples, n_features) Samples.
Returns:	C : array of shape (n_samples,) Predicted class label for each sample.

predict_proba(self, X) [source]

Probability estimates.

Parameters:	X : array-like of shape (n_samples, n_features) Samples.
Returns:	T : array-like of shape (n_samples, n_classes) Probability of the sample for each classes on the model, ordered by self.classes_.

fast_automl.baseline.BaselineRegressor

Predicts the mean target value.

Attributes:	y_mean_ : np.array Average target value.

Examples

from fast_automl.baseline import BaselineRegressor

from sklearn.datasets import load_boston
from sklearn.model_selection import train_test_split

X, y = load_boston(return_X_y=True)
X_train, X_test, y_train, y_test = train_test_split(X, y)
reg = BaselineRegressor().fit(X_train, y_train)
reg.score(X_test, y_test)

Methods

fit(self, X, y, sample_weight=None) [source]

Parameters:	X : array-like of shape (n_samples, n_features) Training data. y : array-like of shape (n_samples, n_targets) Target values. sample_weight, array-like of shape (n_samples,), default=Noone : Individual weights for each sample.
Returns:	self :

predict(self, X) [source]

Parameters:	X : array-like, shape (n_samples, n_features) Samples.
Returns:	C : array, shape (n_samples, n_targets) Predicted values