sklearn.metrics.adjusted_mutual_info_score(labels_true, labels_pred)

sklearn.datasets.load_linnerud(return_X_y=False)

class sklearn.linear_model.RANSACRegressor(base_estimator=None, min_samples=None, residual_threshold=None, is_data_valid=None

class sklearn.linear_model.RandomizedLasso(alpha='aic', scaling=0.5, sample_fraction=0.75, n_resampling=200, selection_threshold=0

class sklearn.base.RegressorMixin

Example of Receiver Operating Characteristic (ROC) metric to evaluate classifier output quality. ROC curves typically feature true positive rate

In this example we compare the various initialization strategies for K-means in terms of runtime and quality of the results.

Semi-supervised learning is a situation in which

sklearn.datasets.make_gaussian_quantiles(mean=None, cov=1.0, n_samples=100, n_features=2, n_classes=3, shuffle=True, r

Shows how to use a function transformer in a pipeline. If you know your dataset?s first principle component is irrelevant for a classification task