linear_model¶
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from sklearn import linear_model
dir(linear_model)
from sklearn import linear_model
dir(linear_model)
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['ARDRegression', 'BayesianRidge', 'ElasticNet', 'ElasticNetCV', 'GammaRegressor', 'Hinge', 'Huber', 'HuberRegressor', 'Lars', 'LarsCV', 'Lasso', 'LassoCV', 'LassoLars', 'LassoLarsCV', 'LassoLarsIC', 'LinearRegression', 'Log', 'LogisticRegression', 'LogisticRegressionCV', 'ModifiedHuber', 'MultiTaskElasticNet', 'MultiTaskElasticNetCV', 'MultiTaskLasso', 'MultiTaskLassoCV', 'OrthogonalMatchingPursuit', 'OrthogonalMatchingPursuitCV', 'PassiveAggressiveClassifier', 'PassiveAggressiveRegressor', 'Perceptron', 'PoissonRegressor', 'QuantileRegressor', 'RANSACRegressor', 'Ridge', 'RidgeCV', 'RidgeClassifier', 'RidgeClassifierCV', 'SGDClassifier', 'SGDOneClassSVM', 'SGDRegressor', 'SquaredLoss', 'TheilSenRegressor', 'TweedieRegressor', '__all__', '__builtins__', '__cached__', '__doc__', '__file__', '__loader__', '__name__', '__package__', '__path__', '__spec__', '_base', '_bayes', '_cd_fast', '_coordinate_descent', '_glm', '_huber', '_least_angle', '_linear_loss', '_logistic', '_omp', '_passive_aggressive', '_perceptron', '_quantile', '_ransac', '_ridge', '_sag', '_sag_fast', '_sgd_fast', '_stochastic_gradient', '_theil_sen', 'enet_path', 'lars_path', 'lars_path_gram', 'lasso_path', 'orthogonal_mp', 'orthogonal_mp_gram', 'ridge_regression']
metrics¶
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from sklearn import metrics
dir(metrics)
from sklearn import metrics
dir(metrics)
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['ConfusionMatrixDisplay', 'DetCurveDisplay', 'DistanceMetric', 'PrecisionRecallDisplay', 'PredictionErrorDisplay', 'RocCurveDisplay', '__all__', '__builtins__', '__cached__', '__doc__', '__file__', '__loader__', '__name__', '__package__', '__path__', '__spec__', '_base', '_classification', '_dist_metrics', '_pairwise_distances_reduction', '_pairwise_fast', '_plot', '_ranking', '_regression', '_scorer', 'accuracy_score', 'adjusted_mutual_info_score', 'adjusted_rand_score', 'auc', 'average_precision_score', 'balanced_accuracy_score', 'brier_score_loss', 'calinski_harabasz_score', 'check_scoring', 'class_likelihood_ratios', 'classification_report', 'cluster', 'cohen_kappa_score', 'completeness_score', 'confusion_matrix', 'consensus_score', 'coverage_error', 'd2_absolute_error_score', 'd2_log_loss_score', 'd2_pinball_score', 'd2_tweedie_score', 'davies_bouldin_score', 'dcg_score', 'det_curve', 'euclidean_distances', 'explained_variance_score', 'f1_score', 'fbeta_score', 'fowlkes_mallows_score', 'get_scorer', 'get_scorer_names', 'hamming_loss', 'hinge_loss', 'homogeneity_completeness_v_measure', 'homogeneity_score', 'jaccard_score', 'label_ranking_average_precision_score', 'label_ranking_loss', 'log_loss', 'make_scorer', 'matthews_corrcoef', 'max_error', 'mean_absolute_error', 'mean_absolute_percentage_error', 'mean_gamma_deviance', 'mean_pinball_loss', 'mean_poisson_deviance', 'mean_squared_error', 'mean_squared_log_error', 'mean_tweedie_deviance', 'median_absolute_error', 'multilabel_confusion_matrix', 'mutual_info_score', 'nan_euclidean_distances', 'ndcg_score', 'normalized_mutual_info_score', 'pair_confusion_matrix', 'pairwise', 'pairwise_distances', 'pairwise_distances_argmin', 'pairwise_distances_argmin_min', 'pairwise_distances_chunked', 'pairwise_kernels', 'precision_recall_curve', 'precision_recall_fscore_support', 'precision_score', 'r2_score', 'rand_score', 'recall_score', 'roc_auc_score', 'roc_curve', 'root_mean_squared_error', 'root_mean_squared_log_error', 'silhouette_samples', 'silhouette_score', 'top_k_accuracy_score', 'v_measure_score', 'zero_one_loss']
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