Warning DEPRECATED class sklearn.qda.QDA(priors=None, reg_param=0.0, store_covariances=False, tol=0.0001) [source] Alias for sklearn.discriminant_analysis.QuadraticDiscriminantAnalysis. Deprecated since version 0.17: This class will be removed in 0.19. Use sklearn.discriminant_analysis.QuadraticDiscriminantAnalysis instead. Methods decision_function(X) Apply decision function to an array of samples. fit(X, y[, store_covariances, tol]) Fit the model according to the given training data

Plot the confidence ellipsoids of each class and decision boundary print(__doc__) from scipy import linalg import numpy as np import matplotlib.pyplot as plt import matplotlib as mpl from matplotlib import colors from sklearn.discriminant_analysis import LinearDiscriminantAnalysis from sklearn.discriminant_analysis import QuadraticDiscriminantAnalysis colormap cmap = colors.LinearSegmentedColormap( 'red_blue_classes', {'red': [(0, 1, 1), (1, 0.7, 0.7)], 'green': [(0, 0.7, 0.7),

Plot the class probabilities of the first sample in a toy dataset predicted by three different classifiers and averaged by the VotingClassifier. First, three examplary classifiers are initialized (LogisticRegression, GaussianNB, and RandomForestClassifier) and used to initialize a soft-voting VotingClassifier with weights [1, 1, 5], which means that the predicted probabilities of the RandomForestClassifier count 5 times as much as the weights of the other classifiers when the averaged probabil

sklearn.metrics.classification_report(y_true, y_pred, labels=None, target_names=None, sample_weight=None, digits=2) [source] Build a text report showing the main classification metrics Read more in the User Guide. Parameters: y_true : 1d array-like, or label indicator array / sparse matrix Ground truth (correct) target values. y_pred : 1d array-like, or label indicator array / sparse matrix Estimated targets as returned by a classifier. labels : array, shape = [n_labels] Optional list

sklearn.linear_model.lasso_stability_path(X, y, scaling=0.5, random_state=None, n_resampling=200, n_grid=100, sample_fraction=0.75, eps=8.8817841970012523e-16, n_jobs=1, verbose=False) [source] Stability path based on randomized Lasso estimates Read more in the User Guide. Parameters: X : array-like, shape = [n_samples, n_features] training data. y : array-like, shape = [n_samples] target values. scaling : float, optional, default=0.5 The alpha parameter in the stability selection art

class sklearn.model_selection.RandomizedSearchCV(estimator, param_distributions, n_iter=10, scoring=None, fit_params=None, n_jobs=1, iid=True, refit=True, cv=None, verbose=0, pre_dispatch='2*n_jobs', random_state=None, error_score='raise', return_train_score=True) [source] Randomized search on hyper parameters. RandomizedSearchCV implements a ?fit? and a ?score? method. It also implements ?predict?, ?predict_proba?, ?decision_function?, ?transform? and ?inverse_transform? if they are implem

An example to compare multi-output regression with random forest and the multioutput.MultiOutputRegressor meta-estimator. This example illustrates the use of the multioutput.MultiOutputRegressor meta-estimator to perform multi-output regression. A random forest regressor is used, which supports multi-output regression natively, so the results can be compared. The random forest regressor will only ever predict values within the range of observations or closer to zero for each of the targets. As

class sklearn.cross_decomposition.PLSSVD(n_components=2, scale=True, copy=True) [source] Partial Least Square SVD Simply perform a svd on the crosscovariance matrix: X?Y There are no iterative deflation here. Read more in the User Guide. Parameters: n_components : int, default 2 Number of components to keep. scale : boolean, default True Whether to scale X and Y. copy : boolean, default True Whether to copy X and Y, or perform in-place computations. Attributes: x_weights_ : array,

class sklearn.ensemble.BaggingRegressor(base_estimator=None, n_estimators=10, max_samples=1.0, max_features=1.0, bootstrap=True, bootstrap_features=False, oob_score=False, warm_start=False, n_jobs=1, random_state=None, verbose=0) [source] A Bagging regressor. A Bagging regressor is an ensemble meta-estimator that fits base regressors each on random subsets of the original dataset and then aggregate their individual predictions (either by voting or by averaging) to form a final prediction. S

There are 3 different approaches to evaluate the quality of predictions of a model: Estimator score method: Estimators have a score method providing a default evaluation criterion for the problem they are designed to solve. This is not discussed on this page, but in each estimator?s documentation. Scoring parameter: Model-evaluation tools using cross-validation (such as model_selection.cross_val_score and model_selection.GridSearchCV) rely on an internal scoring strategy. This is discussed i