make_scorer sklearn example

Motivation: Search in the parameter space to find the best parameters choice for optics (or dbscan) model. Scoring function to compute the LIFT metric, the ratio of correctly predicted positive examples and the actual positive examples in the test dataset. So an algorithm such as OPTICS may not be a good example for this usecase. Other examples are. While common, MSE isn't necessarily the best error metric for your problem. Scikit learn Classification Metrics. Well occasionally send you account related emails. What is the motivation of using cross-validation in this setting? Have a question about this project? The function uses the default scoring method for each model. There is no notion of training and test set in your code, And the way you define training and test score are confusing. The simple approaches are. And @jnothman has thought about this pretty in-depth, I think. # Here are some parameters to search over. For this particular loss, you can use SGDRegressor to minimize MAE. The classification metrics is a process that requires probability evaluation of the positive class. By voting up you can indicate which examples are most useful and appropriate. If the score you want isn't on that list, then you can build a custom scorer. Consider a classifier for determining if someone had a disease, and we are aiming for high recall (i.e. We can raise a better error message there. If you use MSE as your loss, and MAE as your scoring, you are unlikely to find the best answer. sklearn.metrics.make_scorer sklearn.metrics.make_scorer (score_func, *, greater_is_better=True, needs_proba=False, needs_threshold=False, **kwargs) [source] Make a scorer from a performance metric or loss function. This isn't fundamentally any different from what is happening when we find coefficients using MSE and then select the model with the lowest MAE, instead of using MAE as both the loss and the scoring. In the standard implementation, it is assumed that the a higher score is better, which is why we see the functions we want to minimize appear in the negative form, such as neg_mean_absolute_error: minimizing the mean absolute error is the same as maximizing the negative of the mean absolute error. In the following code, we will import classification_report from sklearn.metrics by which we can calculate the worth of the prediction from the algorithm of classification. I've tried all clustering metrics from sklearn.metrics. The classification metrics is a process that requires probability evaluation of the positive class. For instance, if I use LASSO and get a vector of predicted values y, I will do something like y [y<0]=0 before evaluating the success of the model. Whether score_func requires predict_proba to get probability estimates out of a classifier. A classification is a form of data analysis that extracts models describing important data classes. If None, then features are scaled by a random value drawn in [1, 100]. #4301. Valid metrics for pairwise_kernels This function simply returns the valid pairwise distance me, sklearn.metrics.roc_curve(y_true, y_score, pos_label=None, sample_weight=None, drop_intermediate=True) [source] Multiclass and multioutput algorithms, 1.2. Using make_scorer() for a GridSearchCV scoring parameter in a clustering task, # data: A dataframe with two columns (x, y), # return clusters corresponding to (x, y) pairs according to "optics" algorithm, # w.r.t. This factory function wraps scoring functions for use in GridSearchCV and cross_val_score. You can vote up the ones you like or vote down the ones you don't like, and go to the original project or source file by following the links above each example. For example, if the probability is higher than 0.1, the class is predicted negative else positive. The object to use to fit the data. It takes a score function, such as accuracy_score, mean_squared_error, adjusted_rand_index or average_precision and returns a callable that scores an estimators output. In the following code, we will import accuracy_score from sklearn.metrics that implement score, probability functions to calculate classification performance. accuracy_score(ytrue, ypred) is used to calculate the accuracy score. scoring : str or callable, default=None. @adrinjalali @amueller Sign in That is, when I am not off taking pictures somewhere! In this section, we will learn how scikit learn classification report support works in python. This factory function wraps scoring functions for use in GridSearchCV and cross_val_score. : , : What I would like to do is to have my scoring function take in the probability prediction, actual label and ideally the decile threshold in percentage. privacy statement. ~ For each possible choice of parameters from the parameters grid space, say p: Saying "GridSearchCV should support clustering estimators as well." It must be worked for either case, with/without ground truth. Read more in the User Guide. Make a scorer from a performance metric or loss function. I have a machine learning model where unphysical values are modified before scoring. You signed in with another tab or window. Python SCORERS - 5 examples found. The difference is that recall is a bad loss function because it is trivial to optimize. This gives a nice distinction between a loss (used when fitting) and a score (used when choosing between fit models). Earn . Whether score_func is a score function (default), meaning high is good, or a loss function, meaning low is good. As @amueller mentioned, having the scorer call fit_predict is probably not what you want to do, since it'd be ignoring your training set. By default make_scorer uses predict, which OPTICS doesn't have. If needs_proba=False and needs_threshold=False, the score function is supposed to accept the output of predict. Classification is a bunch of different classes and sorting these classes into different categories. In this section, we will learn about how the scikit learn classification report works in python. It takes a score function, such as accuracy_score , mean_squared_error , adjusted_rand_score or average_precision_score and returns a callable that scores an estimator's output. Valid metrics for pairwise_distances. This sounds complicated, but let's build mean absolute error as a scorer to see how it would work. Goal: Finding the best parameters (w.r.t. I am using scikit-learn and would like to use sklearn.model_selection.cross_validate to do cross-validation. In this section, we will learn about how scikit learn classification accuracy works in python. TypeError: _score() missing 1 required positional argument: 'y_true', Even by using grid_search_cv.fit(data, labels) instead of grid_search_cv.fit(data), another exception rised: Partial Dependence and Individual Conditional Expectation plots, 6.5. In the context of classification, lift [1] compares model predictions to randomly generated predictions. If True, for binary y_true, the score function is supposed to accept a 1D y_pred (i.e., probability of the positive class or the decision function, shape (n_samples,)). Scikit learn Classification Report Support, module matplotlib has no attribute artist, Scikit learn classification report support. So indeed that could be seen as a limitation of make_scorer but it's not really the core issue. In this section, we will learn about scikit learn classification example works in python. olay regenerist retinol 24 night; bannerlord essential mods 2022 reddit; high ketones and specific gravity in urine After running the above code, we get the following output in which we can see that we have a different classifier and we sorted this classification into different categories. For example average_precision or the area under the roc curve can not be computed using discrete predictions alone. The make_scorer function allows us to specify directly whether we should maximize or minimize. Python make_scorer - 30 examples found. This sounds complicated, but let's build mean absolute error as a scorer to see how it would work. The fit() method of GridSearchCV automatically handles the type of the estimator which passed to its constructor, for example, for a clustering estimator it considers labels_ instead of predict() for scoring. In this section, we will learn about how scikit learn classification tree works in python. After all, if we are going to optimize for something, wouldn't it make sense to optimize for it throughout? Here are the examples of the python api sklearn.metrics.make_scorer taken from open source projects. The default scoring parameters don't work across all models, so you have to define your own metrics. It takes a score function, such as accuracy_score , mean_squared_error, adjusted_rand_index or average_precision and returns a callable that scores an estimator's output. A loss function can be called thousands of times on a single model to find its parameters (the number of tiems called depends on max_tol and max_iterations parameters to the estimators). The main question is "What do you want to do" and I don't see an answer to that in your post. Write a custom loss in Keras. Classification is a form of data analysis that extracts models describing important data classes. In the following code, we will import some libraries from which we can perform the classification task. Non-numeric features generally have to be encoded into one or more numeric features A definition cannot be wrong, but it can fail to be useful. In the following code, we will import gaussianProcessClassifier from sklearn.gaussian_process also import matplotlib.pyplot as plot by which we plot the probability classes. scoring=ftwo_scorer) AttributeError: 'OPTICS' object has no attribute 'predict'. After running the above code, we get the following output in which we can see that the cross value score is printed on the screen. Whether score_func is a score function (default), meaning high is good, or a loss function, meaning low is good. Instead, in a given problem, I should more carefully consider the trade-offs between false positives and false negatives, and use that to pick an appropriate scoring method. 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the iris dataset, Example: Plot individual and voting regression predictions, Example: Plot multi-class SGD on the iris dataset, Example: Plot multinomial and One-vs-Rest Logistic Regression, Example: Plot randomly generated classification dataset, Example: Plot randomly generated multilabel dataset, Example: Plot the decision boundaries of a VotingClassifier, Example: Plot the decision surface of a decision tree on the iris dataset, Example: Plot the decision surfaces of ensembles of trees on the iris dataset, Example: Plot the support vectors in LinearSVC, Example: Plotting Cross-Validated Predictions, Example: Poisson regression and non-normal loss, Example: Post pruning decision trees with cost complexity pruning, Example: Prediction Intervals for Gradient Boosting Regression, Example: Principal Component Regression vs Partial Least Squares Regression, Example: Probabilistic predictions with Gaussian process classification, Example: Probability Calibration for 3-class classification, Example: Probability calibration of classifiers, Example: ROC Curve with Visualization API, Example: Receiver Operating Characteristic, Example: Receiver Operating Characteristic with cross validation, Example: Recursive feature elimination with cross-validation, Example: Regularization path of L1- Logistic Regression, Example: Release Highlights for scikit-learn 0.22, Example: Release Highlights for scikit-learn 0.23, Example: Release Highlights for scikit-learn 0.24, Example: Restricted Boltzmann Machine features for digit classification, Example: Robust covariance estimation and Mahalanobis distances relevance, Example: Robust linear model estimation using RANSAC, Example: Robust vs Empirical covariance estimate, Example: SGD: Maximum margin separating hyperplane, Example: SVM: Maximum margin separating hyperplane, Example: SVM: Separating hyperplane for unbalanced classes, Example: Sample pipeline for text feature extraction and evaluation, Example: Scalable learning with polynomial kernel aproximation, Example: Scaling the regularization parameter for SVCs, Example: Segmenting the picture of greek coins in regions, Example: Selecting dimensionality reduction with Pipeline and GridSearchCV, Example: Selecting the number of clusters with silhouette analysis on KMeans clustering, Example: Semi-supervised Classification on a Text Dataset, Example: Simple 1D Kernel Density Estimation, Example: Sparse coding with a precomputed dictionary, Example: Sparse inverse covariance estimation, Example: Spectral clustering for image segmentation, Example: Statistical comparison of models using grid search, Example: Support Vector Regression using linear and non-linear kernels, Example: Test with permutations the significance of a classification score, Example: The Johnson-Lindenstrauss bound for embedding with random projections, Example: Topic extraction with Non-negative Matrix Factorization and Latent Dirichlet Allocation, Example: Tweedie regression on insurance claims, Example: Understanding the decision tree structure, Example: Using KBinsDiscretizer to discretize continuous features, Example: Various Agglomerative Clustering on a 2D embedding of digits, Example: Varying regularization in Multi-layer Perceptron, Example: Visualization of MLP weights on MNIST, Example: Visualizations with Display Objects, Example: Visualizing cross-validation behavior in scikit-learn, Example: Visualizing the stock market structure, Example: t-SNE: The effect of various perplexity values on the shape, calibration.CalibratedClassifierCV.get_params(), calibration.CalibratedClassifierCV.predict(), calibration.CalibratedClassifierCV.predict_proba(), calibration.CalibratedClassifierCV.score(), calibration.CalibratedClassifierCV.set_params(), cluster.AffinityPropagation.fit_predict(), cluster.AgglomerativeClustering.fit_predict(), cluster.AgglomerativeClustering.get_params(), cluster.AgglomerativeClustering.set_params(), 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feature_selection.SelectKBest.inverse_transform(), feature_selection.SelectKBest.set_params(), feature_selection.SelectKBest.transform(), feature_selection.SelectPercentile.fit_transform(), feature_selection.SelectPercentile.get_params(), feature_selection.SelectPercentile.get_support(), feature_selection.SelectPercentile.inverse_transform(), feature_selection.SelectPercentile.set_params(), feature_selection.SelectPercentile.transform(), feature_selection.SelectorMixin.fit_transform(), feature_selection.SelectorMixin.get_support(), feature_selection.SelectorMixin.inverse_transform(), feature_selection.SelectorMixin.transform(), feature_selection.SequentialFeatureSelector, feature_selection.SequentialFeatureSelector(), feature_selection.SequentialFeatureSelector.fit(), feature_selection.SequentialFeatureSelector.fit_transform(), feature_selection.SequentialFeatureSelector.get_params(), feature_selection.SequentialFeatureSelector.get_support(), feature_selection.SequentialFeatureSelector.inverse_transform(), feature_selection.SequentialFeatureSelector.set_params(), feature_selection.SequentialFeatureSelector.transform(), feature_selection.VarianceThreshold.fit(), feature_selection.VarianceThreshold.fit_transform(), feature_selection.VarianceThreshold.get_params(), feature_selection.VarianceThreshold.get_support(), feature_selection.VarianceThreshold.inverse_transform(), feature_selection.VarianceThreshold.set_params(), feature_selection.VarianceThreshold.transform(), feature_selection.mutual_info_regression(), sklearn.feature_selection.mutual_info_classif(), sklearn.feature_selection.mutual_info_regression(), gaussian_process.GaussianProcessClassifier, gaussian_process.GaussianProcessClassifier(), gaussian_process.GaussianProcessClassifier.fit(), gaussian_process.GaussianProcessClassifier.get_params(), gaussian_process.GaussianProcessClassifier.log_marginal_likelihood(), gaussian_process.GaussianProcessClassifier.predict(), gaussian_process.GaussianProcessClassifier.predict_proba(), gaussian_process.GaussianProcessClassifier.score(), gaussian_process.GaussianProcessClassifier.set_params(), gaussian_process.GaussianProcessRegressor, gaussian_process.GaussianProcessRegressor(), gaussian_process.GaussianProcessRegressor.fit(), gaussian_process.GaussianProcessRegressor.get_params(), gaussian_process.GaussianProcessRegressor.log_marginal_likelihood(), gaussian_process.GaussianProcessRegressor.predict(), gaussian_process.GaussianProcessRegressor.sample_y(), gaussian_process.GaussianProcessRegressor.score(), gaussian_process.GaussianProcessRegressor.set_params(), gaussian_process.kernels.CompoundKernel(), gaussian_process.kernels.CompoundKernel.__call__(), gaussian_process.kernels.CompoundKernel.bounds(), gaussian_process.kernels.CompoundKernel.clone_with_theta(), gaussian_process.kernels.CompoundKernel.diag(), gaussian_process.kernels.CompoundKernel.get_params(), gaussian_process.kernels.CompoundKernel.hyperparameters(), gaussian_process.kernels.CompoundKernel.is_stationary(), gaussian_process.kernels.CompoundKernel.n_dims(), gaussian_process.kernels.CompoundKernel.requires_vector_input(), gaussian_process.kernels.CompoundKernel.set_params(), gaussian_process.kernels.CompoundKernel.theta(), gaussian_process.kernels.ConstantKernel(), gaussian_process.kernels.ConstantKernel.__call__(), gaussian_process.kernels.ConstantKernel.bounds(), gaussian_process.kernels.ConstantKernel.clone_with_theta(), gaussian_process.kernels.ConstantKernel.diag(), gaussian_process.kernels.ConstantKernel.get_params(), gaussian_process.kernels.ConstantKernel.hyperparameters(), gaussian_process.kernels.ConstantKernel.is_stationary(), gaussian_process.kernels.ConstantKernel.n_dims(), gaussian_process.kernels.ConstantKernel.requires_vector_input(), gaussian_process.kernels.ConstantKernel.set_params(), gaussian_process.kernels.ConstantKernel.theta(), gaussian_process.kernels.DotProduct.__call__(), gaussian_process.kernels.DotProduct.bounds(), gaussian_process.kernels.DotProduct.clone_with_theta(), gaussian_process.kernels.DotProduct.diag(), gaussian_process.kernels.DotProduct.get_params(), gaussian_process.kernels.DotProduct.hyperparameters(), gaussian_process.kernels.DotProduct.is_stationary(), gaussian_process.kernels.DotProduct.n_dims(), gaussian_process.kernels.DotProduct.requires_vector_input(), gaussian_process.kernels.DotProduct.set_params(), gaussian_process.kernels.DotProduct.theta(), gaussian_process.kernels.ExpSineSquared(), gaussian_process.kernels.ExpSineSquared.__call__(), gaussian_process.kernels.ExpSineSquared.bounds(), gaussian_process.kernels.ExpSineSquared.clone_with_theta(), gaussian_process.kernels.ExpSineSquared.diag(), gaussian_process.kernels.ExpSineSquared.get_params(), gaussian_process.kernels.ExpSineSquared.hyperparameter_length_scale(), gaussian_process.kernels.ExpSineSquared.hyperparameters(), gaussian_process.kernels.ExpSineSquared.is_stationary(), gaussian_process.kernels.ExpSineSquared.n_dims(), gaussian_process.kernels.ExpSineSquared.requires_vector_input(), gaussian_process.kernels.ExpSineSquared.set_params(), gaussian_process.kernels.ExpSineSquared.theta(), gaussian_process.kernels.Exponentiation(), gaussian_process.kernels.Exponentiation.__call__(), gaussian_process.kernels.Exponentiation.bounds(), gaussian_process.kernels.Exponentiation.clone_with_theta(), gaussian_process.kernels.Exponentiation.diag(), gaussian_process.kernels.Exponentiation.get_params(), gaussian_process.kernels.Exponentiation.hyperparameters(), gaussian_process.kernels.Exponentiation.is_stationary(), gaussian_process.kernels.Exponentiation.n_dims(), gaussian_process.kernels.Exponentiation.requires_vector_input(), gaussian_process.kernels.Exponentiation.set_params(), gaussian_process.kernels.Exponentiation.theta(), gaussian_process.kernels.Hyperparameter(), 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gaussian_process.kernels.Matern.requires_vector_input(), gaussian_process.kernels.Matern.set_params(), gaussian_process.kernels.PairwiseKernel(), gaussian_process.kernels.PairwiseKernel.__call__(), gaussian_process.kernels.PairwiseKernel.bounds(), gaussian_process.kernels.PairwiseKernel.clone_with_theta(), gaussian_process.kernels.PairwiseKernel.diag(), gaussian_process.kernels.PairwiseKernel.get_params(), gaussian_process.kernels.PairwiseKernel.hyperparameters(), gaussian_process.kernels.PairwiseKernel.is_stationary(), gaussian_process.kernels.PairwiseKernel.n_dims(), gaussian_process.kernels.PairwiseKernel.requires_vector_input(), gaussian_process.kernels.PairwiseKernel.set_params(), gaussian_process.kernels.PairwiseKernel.theta(), gaussian_process.kernels.Product.__call__(), gaussian_process.kernels.Product.bounds(), gaussian_process.kernels.Product.clone_with_theta(), gaussian_process.kernels.Product.get_params(), gaussian_process.kernels.Product.hyperparameters(), gaussian_process.kernels.Product.is_stationary(), gaussian_process.kernels.Product.n_dims(), gaussian_process.kernels.Product.requires_vector_input(), gaussian_process.kernels.Product.set_params(), gaussian_process.kernels.RBF.clone_with_theta(), gaussian_process.kernels.RBF.get_params(), gaussian_process.kernels.RBF.hyperparameters(), gaussian_process.kernels.RBF.is_stationary(), gaussian_process.kernels.RBF.requires_vector_input(), gaussian_process.kernels.RBF.set_params(), gaussian_process.kernels.RationalQuadratic, gaussian_process.kernels.RationalQuadratic(), gaussian_process.kernels.RationalQuadratic.__call__(), gaussian_process.kernels.RationalQuadratic.bounds(), gaussian_process.kernels.RationalQuadratic.clone_with_theta(), gaussian_process.kernels.RationalQuadratic.diag(), gaussian_process.kernels.RationalQuadratic.get_params(), gaussian_process.kernels.RationalQuadratic.hyperparameters(), gaussian_process.kernels.RationalQuadratic.is_stationary(), gaussian_process.kernels.RationalQuadratic.n_dims(), gaussian_process.kernels.RationalQuadratic.requires_vector_input(), gaussian_process.kernels.RationalQuadratic.set_params(), gaussian_process.kernels.RationalQuadratic.theta(), gaussian_process.kernels.Sum.clone_with_theta(), gaussian_process.kernels.Sum.get_params(), gaussian_process.kernels.Sum.hyperparameters(), gaussian_process.kernels.Sum.is_stationary(), gaussian_process.kernels.Sum.requires_vector_input(), gaussian_process.kernels.Sum.set_params(), gaussian_process.kernels.WhiteKernel.__call__(), gaussian_process.kernels.WhiteKernel.bounds(), gaussian_process.kernels.WhiteKernel.clone_with_theta(), gaussian_process.kernels.WhiteKernel.diag(), gaussian_process.kernels.WhiteKernel.get_params(), gaussian_process.kernels.WhiteKernel.hyperparameters(), gaussian_process.kernels.WhiteKernel.is_stationary(), gaussian_process.kernels.WhiteKernel.n_dims(), gaussian_process.kernels.WhiteKernel.requires_vector_input(), gaussian_process.kernels.WhiteKernel.set_params(), gaussian_process.kernels.WhiteKernel.theta(), inspection.PartialDependenceDisplay.plot(), sklearn.inspection.permutation_importance(), sklearn.inspection.plot_partial_dependence(), isotonic.IsotonicRegression.fit_transform(), kernel_approximation.AdditiveChi2Sampler(), kernel_approximation.AdditiveChi2Sampler.fit(), kernel_approximation.AdditiveChi2Sampler.fit_transform(), kernel_approximation.AdditiveChi2Sampler.get_params(), kernel_approximation.AdditiveChi2Sampler.set_params(), kernel_approximation.AdditiveChi2Sampler.transform(), kernel_approximation.Nystroem.fit_transform(), kernel_approximation.Nystroem.get_params(), kernel_approximation.Nystroem.set_params(), kernel_approximation.Nystroem.transform(), kernel_approximation.PolynomialCountSketch, kernel_approximation.PolynomialCountSketch(), kernel_approximation.PolynomialCountSketch.fit(), kernel_approximation.PolynomialCountSketch.fit_transform(), kernel_approximation.PolynomialCountSketch.get_params(), kernel_approximation.PolynomialCountSketch.set_params(), kernel_approximation.PolynomialCountSketch.transform(), kernel_approximation.RBFSampler.fit_transform(), kernel_approximation.RBFSampler.get_params(), kernel_approximation.RBFSampler.set_params(), kernel_approximation.RBFSampler.transform(), kernel_approximation.SkewedChi2Sampler.fit(), kernel_approximation.SkewedChi2Sampler.fit_transform(), kernel_approximation.SkewedChi2Sampler.get_params(), kernel_approximation.SkewedChi2Sampler.set_params(), kernel_approximation.SkewedChi2Sampler.transform(), linear_model.LinearRegression.get_params(), linear_model.LinearRegression.set_params(), linear_model.LogisticRegression.decision_function(), linear_model.LogisticRegression.densify(), linear_model.LogisticRegression.get_params(), linear_model.LogisticRegression.predict(), linear_model.LogisticRegression.predict_log_proba(), linear_model.LogisticRegression.predict_proba(), linear_model.LogisticRegression.set_params(), linear_model.LogisticRegression.sparsify(), linear_model.LogisticRegressionCV.decision_function(), linear_model.LogisticRegressionCV.densify(), linear_model.LogisticRegressionCV.get_params(), linear_model.LogisticRegressionCV.predict(), linear_model.LogisticRegressionCV.predict_log_proba(), linear_model.LogisticRegressionCV.predict_proba(), linear_model.LogisticRegressionCV.score(), linear_model.LogisticRegressionCV.set_params(), linear_model.LogisticRegressionCV.sparsify(), linear_model.MultiTaskElasticNet.get_params(), linear_model.MultiTaskElasticNet.predict(), linear_model.MultiTaskElasticNet.set_params(), linear_model.MultiTaskElasticNet.sparse_coef_(), linear_model.MultiTaskElasticNetCV.get_params(), linear_model.MultiTaskElasticNetCV.path(), linear_model.MultiTaskElasticNetCV.predict(), linear_model.MultiTaskElasticNetCV.score(), linear_model.MultiTaskElasticNetCV.set_params(), linear_model.MultiTaskLasso.sparse_coef_(), linear_model.MultiTaskLassoCV.get_params(), linear_model.MultiTaskLassoCV.set_params(), linear_model.OrthogonalMatchingPursuit.fit(), linear_model.OrthogonalMatchingPursuit.get_params(), linear_model.OrthogonalMatchingPursuit.predict(), linear_model.OrthogonalMatchingPursuit.score(), linear_model.OrthogonalMatchingPursuit.set_params(), linear_model.OrthogonalMatchingPursuitCV(), linear_model.OrthogonalMatchingPursuitCV.fit(), linear_model.OrthogonalMatchingPursuitCV.get_params(), linear_model.OrthogonalMatchingPursuitCV.predict(), linear_model.OrthogonalMatchingPursuitCV.score(), linear_model.OrthogonalMatchingPursuitCV.set_params(), linear_model.PassiveAggressiveClassifier(), linear_model.PassiveAggressiveClassifier.decision_function(), linear_model.PassiveAggressiveClassifier.densify(), linear_model.PassiveAggressiveClassifier.fit(), linear_model.PassiveAggressiveClassifier.get_params(), linear_model.PassiveAggressiveClassifier.partial_fit(), 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linear_model.TweedieRegressor.get_params(), linear_model.TweedieRegressor.set_params(), sklearn.linear_model.PassiveAggressiveRegressor(), sklearn.linear_model.orthogonal_mp_gram(), manifold.LocallyLinearEmbedding.fit_transform(), manifold.LocallyLinearEmbedding.get_params(), manifold.LocallyLinearEmbedding.set_params(), manifold.LocallyLinearEmbedding.transform(), manifold.SpectralEmbedding.fit_transform(), sklearn.manifold.locally_linear_embedding(), metrics.homogeneity_completeness_v_measure(), metrics.label_ranking_average_precision_score(), metrics.precision_recall_fscore_support(), sklearn.metrics.adjusted_mutual_info_score(), sklearn.metrics.average_precision_score(), sklearn.metrics.balanced_accuracy_score(), sklearn.metrics.calinski_harabasz_score(), sklearn.metrics.explained_variance_score(), sklearn.metrics.homogeneity_completeness_v_measure(), sklearn.metrics.label_ranking_average_precision_score(), sklearn.metrics.mean_absolute_percentage_error(), sklearn.metrics.multilabel_confusion_matrix(), sklearn.metrics.normalized_mutual_info_score(), sklearn.metrics.pairwise_distances_argmin(), sklearn.metrics.pairwise_distances_argmin_min(), sklearn.metrics.pairwise_distances_chunked(), sklearn.metrics.plot_precision_recall_curve(), sklearn.metrics.precision_recall_fscore_support(), sklearn.metrics.cluster.contingency_matrix(), sklearn.metrics.cluster.pair_confusion_matrix(), metrics.pairwise.nan_euclidean_distances(), metrics.pairwise.paired_cosine_distances(), metrics.pairwise.paired_euclidean_distances(), metrics.pairwise.paired_manhattan_distances(), sklearn.metrics.pairwise.additive_chi2_kernel(), sklearn.metrics.pairwise.cosine_distances(), sklearn.metrics.pairwise.cosine_similarity(), sklearn.metrics.pairwise.distance_metrics(), sklearn.metrics.pairwise.euclidean_distances(), sklearn.metrics.pairwise.haversine_distances(), sklearn.metrics.pairwise.kernel_metrics(), sklearn.metrics.pairwise.laplacian_kernel(), sklearn.metrics.pairwise.manhattan_distances(), sklearn.metrics.pairwise.nan_euclidean_distances(), sklearn.metrics.pairwise.paired_cosine_distances(), sklearn.metrics.pairwise.paired_distances(), sklearn.metrics.pairwise.paired_euclidean_distances(), sklearn.metrics.pairwise.paired_manhattan_distances(), sklearn.metrics.pairwise.pairwise_kernels(), sklearn.metrics.pairwise.polynomial_kernel(), sklearn.metrics.pairwise.sigmoid_kernel(), mixture.BayesianGaussianMixture.fit_predict(), mixture.BayesianGaussianMixture.get_params(), mixture.BayesianGaussianMixture.predict(), mixture.BayesianGaussianMixture.predict_proba(), mixture.BayesianGaussianMixture.score_samples(), mixture.BayesianGaussianMixture.set_params(), model_selection.GridSearchCV.decision_function(), model_selection.GridSearchCV.get_params(), model_selection.GridSearchCV.inverse_transform(), model_selection.GridSearchCV.predict_log_proba(), model_selection.GridSearchCV.predict_proba(), model_selection.GridSearchCV.score_samples(), model_selection.GridSearchCV.set_params(), model_selection.GroupKFold.get_n_splits(), model_selection.GroupShuffleSplit.get_n_splits(), model_selection.GroupShuffleSplit.split(), model_selection.HalvingGridSearchCV.decision_function(), model_selection.HalvingGridSearchCV.fit(), model_selection.HalvingGridSearchCV.get_params(), model_selection.HalvingGridSearchCV.inverse_transform(), model_selection.HalvingGridSearchCV.predict(), model_selection.HalvingGridSearchCV.predict_log_proba(), model_selection.HalvingGridSearchCV.predict_proba(), model_selection.HalvingGridSearchCV.score(), model_selection.HalvingGridSearchCV.score_samples(), model_selection.HalvingGridSearchCV.set_params(), model_selection.HalvingGridSearchCV.transform(), model_selection.HalvingRandomSearchCV.decision_function(), model_selection.HalvingRandomSearchCV.fit(), model_selection.HalvingRandomSearchCV.get_params(), model_selection.HalvingRandomSearchCV.inverse_transform(), model_selection.HalvingRandomSearchCV.predict(), model_selection.HalvingRandomSearchCV.predict_log_proba(), model_selection.HalvingRandomSearchCV.predict_proba(), model_selection.HalvingRandomSearchCV.score(), model_selection.HalvingRandomSearchCV.score_samples(), model_selection.HalvingRandomSearchCV.set_params(), model_selection.HalvingRandomSearchCV.transform(), model_selection.LeaveOneGroupOut.get_n_splits(), model_selection.LeaveOneOut.get_n_splits(), model_selection.LeavePGroupsOut.get_n_splits(), model_selection.PredefinedSplit.get_n_splits(), model_selection.RandomizedSearchCV.decision_function(), model_selection.RandomizedSearchCV.get_params(), model_selection.RandomizedSearchCV.inverse_transform(), model_selection.RandomizedSearchCV.predict(), model_selection.RandomizedSearchCV.predict_log_proba(), model_selection.RandomizedSearchCV.predict_proba(), model_selection.RandomizedSearchCV.score(), model_selection.RandomizedSearchCV.score_samples(), model_selection.RandomizedSearchCV.set_params(), model_selection.RandomizedSearchCV.transform(), model_selection.RepeatedKFold.get_n_splits(), model_selection.RepeatedStratifiedKFold(), model_selection.RepeatedStratifiedKFold.get_n_splits(), model_selection.RepeatedStratifiedKFold.split(), model_selection.ShuffleSplit.get_n_splits(), model_selection.StratifiedKFold.get_n_splits(), model_selection.StratifiedShuffleSplit.get_n_splits(), model_selection.StratifiedShuffleSplit.split(), model_selection.TimeSeriesSplit.get_n_splits(), sklearn.model_selection.cross_val_predict(), sklearn.model_selection.cross_val_score(), sklearn.model_selection.permutation_test_score(), sklearn.model_selection.train_test_split(), sklearn.model_selection.validation_curve(), multioutput.ClassifierChain.decision_function(), multioutput.ClassifierChain.predict_proba(), multioutput.MultiOutputClassifier.get_params(), multioutput.MultiOutputClassifier.partial_fit(), multioutput.MultiOutputClassifier.predict(), multioutput.MultiOutputClassifier.predict_proba(), multioutput.MultiOutputClassifier.score(), multioutput.MultiOutputClassifier.set_params(), multioutput.MultiOutputRegressor.get_params(), multioutput.MultiOutputRegressor.partial_fit(), multioutput.MultiOutputRegressor.predict(), multioutput.MultiOutputRegressor.set_params(), naive_bayes.BernoulliNB.predict_log_proba(), naive_bayes.CategoricalNB.predict_log_proba(), naive_bayes.CategoricalNB.predict_proba(), naive_bayes.ComplementNB.predict_log_proba(), naive_bayes.GaussianNB.predict_log_proba(), naive_bayes.MultinomialNB.predict_log_proba(), naive_bayes.MultinomialNB.predict_proba(), neighbors.BallTree.two_point_correlation(), neighbors.KNeighborsClassifier.get_params(), neighbors.KNeighborsClassifier.kneighbors(), neighbors.KNeighborsClassifier.kneighbors_graph(), neighbors.KNeighborsClassifier.predict_proba(), neighbors.KNeighborsClassifier.set_params(), neighbors.KNeighborsRegressor.get_params(), neighbors.KNeighborsRegressor.kneighbors(), neighbors.KNeighborsRegressor.kneighbors_graph(), neighbors.KNeighborsRegressor.set_params(), neighbors.KNeighborsTransformer.fit_transform(), neighbors.KNeighborsTransformer.get_params(), neighbors.KNeighborsTransformer.kneighbors(), neighbors.KNeighborsTransformer.kneighbors_graph(), neighbors.KNeighborsTransformer.set_params(), neighbors.KNeighborsTransformer.transform(), neighbors.LocalOutlierFactor.decision_function(), neighbors.LocalOutlierFactor.fit_predict(), neighbors.LocalOutlierFactor.get_params(), neighbors.LocalOutlierFactor.kneighbors(), neighbors.LocalOutlierFactor.kneighbors_graph(), neighbors.LocalOutlierFactor.score_samples(), neighbors.LocalOutlierFactor.set_params(), neighbors.NearestNeighbors.kneighbors_graph(), neighbors.NearestNeighbors.radius_neighbors(), neighbors.NearestNeighbors.radius_neighbors_graph(), neighbors.NeighborhoodComponentsAnalysis(), neighbors.NeighborhoodComponentsAnalysis.fit(), neighbors.NeighborhoodComponentsAnalysis.fit_transform(), neighbors.NeighborhoodComponentsAnalysis.get_params(), neighbors.NeighborhoodComponentsAnalysis.set_params(), neighbors.NeighborhoodComponentsAnalysis.transform(), neighbors.RadiusNeighborsClassifier.fit(), neighbors.RadiusNeighborsClassifier.get_params(), neighbors.RadiusNeighborsClassifier.predict(), neighbors.RadiusNeighborsClassifier.predict_proba(), neighbors.RadiusNeighborsClassifier.radius_neighbors(), neighbors.RadiusNeighborsClassifier.radius_neighbors_graph(), neighbors.RadiusNeighborsClassifier.score(), neighbors.RadiusNeighborsClassifier.set_params(), neighbors.RadiusNeighborsRegressor.get_params(), neighbors.RadiusNeighborsRegressor.predict(), neighbors.RadiusNeighborsRegressor.radius_neighbors(), neighbors.RadiusNeighborsRegressor.radius_neighbors_graph(), neighbors.RadiusNeighborsRegressor.score(), neighbors.RadiusNeighborsRegressor.set_params(), neighbors.RadiusNeighborsTransformer.fit(), neighbors.RadiusNeighborsTransformer.fit_transform(), neighbors.RadiusNeighborsTransformer.get_params(), neighbors.RadiusNeighborsTransformer.radius_neighbors(), neighbors.RadiusNeighborsTransformer.radius_neighbors_graph(), neighbors.RadiusNeighborsTransformer.set_params(), neighbors.RadiusNeighborsTransformer.transform(), sklearn.neighbors.radius_neighbors_graph(), neural_network.BernoulliRBM.fit_transform(), neural_network.BernoulliRBM.partial_fit(), neural_network.BernoulliRBM.score_samples(), neural_network.MLPClassifier.get_params(), neural_network.MLPClassifier.partial_fit(), neural_network.MLPClassifier.predict_log_proba(), neural_network.MLPClassifier.predict_proba(), neural_network.MLPClassifier.set_params(), neural_network.MLPRegressor.partial_fit(), pipeline.FeatureUnion.get_feature_names(), preprocessing.FunctionTransformer.fit_transform(), preprocessing.FunctionTransformer.get_params(), preprocessing.FunctionTransformer.inverse_transform(), preprocessing.FunctionTransformer.set_params(), preprocessing.FunctionTransformer.transform(), preprocessing.KBinsDiscretizer.fit_transform(), preprocessing.KBinsDiscretizer.get_params(), preprocessing.KBinsDiscretizer.inverse_transform(), preprocessing.KBinsDiscretizer.set_params(), preprocessing.KBinsDiscretizer.transform(), preprocessing.KernelCenterer.fit_transform(), preprocessing.KernelCenterer.get_params(), preprocessing.KernelCenterer.set_params(), preprocessing.LabelBinarizer.fit_transform(), preprocessing.LabelBinarizer.get_params(), preprocessing.LabelBinarizer.inverse_transform(), preprocessing.LabelBinarizer.set_params(), preprocessing.LabelEncoder.fit_transform(), preprocessing.LabelEncoder.inverse_transform(), preprocessing.MaxAbsScaler.fit_transform(), preprocessing.MaxAbsScaler.inverse_transform(), preprocessing.MinMaxScaler.fit_transform(), preprocessing.MinMaxScaler.inverse_transform(), preprocessing.MultiLabelBinarizer.fit_transform(), preprocessing.MultiLabelBinarizer.get_params(), preprocessing.MultiLabelBinarizer.inverse_transform(), preprocessing.MultiLabelBinarizer.set_params(), preprocessing.MultiLabelBinarizer.transform(), preprocessing.OneHotEncoder.fit_transform(), preprocessing.OneHotEncoder.get_feature_names(), preprocessing.OneHotEncoder.inverse_transform(), preprocessing.OrdinalEncoder.fit_transform(), preprocessing.OrdinalEncoder.get_params(), preprocessing.OrdinalEncoder.inverse_transform(), preprocessing.OrdinalEncoder.set_params(), preprocessing.PolynomialFeatures.fit_transform(), preprocessing.PolynomialFeatures.get_feature_names(), preprocessing.PolynomialFeatures.get_params(), preprocessing.PolynomialFeatures.set_params(), preprocessing.PolynomialFeatures.transform(), preprocessing.PowerTransformer.fit_transform(), preprocessing.PowerTransformer.get_params(), preprocessing.PowerTransformer.inverse_transform(), preprocessing.PowerTransformer.set_params(), preprocessing.PowerTransformer.transform(), preprocessing.QuantileTransformer.fit_transform(), preprocessing.QuantileTransformer.get_params(), preprocessing.QuantileTransformer.inverse_transform(), preprocessing.QuantileTransformer.set_params(), preprocessing.QuantileTransformer.transform(), preprocessing.RobustScaler.fit_transform(), preprocessing.RobustScaler.inverse_transform(), preprocessing.StandardScaler.fit_transform(), preprocessing.StandardScaler.get_params(), preprocessing.StandardScaler.inverse_transform(), preprocessing.StandardScaler.partial_fit(), preprocessing.StandardScaler.set_params(), sklearn.preprocessing.add_dummy_feature(), sklearn.preprocessing.quantile_transform(), random_projection.GaussianRandomProjection, random_projection.GaussianRandomProjection(), random_projection.GaussianRandomProjection.fit(), random_projection.GaussianRandomProjection.fit_transform(), random_projection.GaussianRandomProjection.get_params(), random_projection.GaussianRandomProjection.set_params(), random_projection.GaussianRandomProjection.transform(), random_projection.SparseRandomProjection(), random_projection.SparseRandomProjection.fit(), random_projection.SparseRandomProjection.fit_transform(), random_projection.SparseRandomProjection.get_params(), random_projection.SparseRandomProjection.set_params(), random_projection.SparseRandomProjection.transform(), random_projection.johnson_lindenstrauss_min_dim(), sklearn.random_projection.johnson_lindenstrauss_min_dim(), semi_supervised.LabelPropagation.get_params(), semi_supervised.LabelPropagation.predict(), semi_supervised.LabelPropagation.predict_proba(), semi_supervised.LabelPropagation.set_params(), semi_supervised.LabelSpreading.get_params(), semi_supervised.LabelSpreading.predict_proba(), semi_supervised.LabelSpreading.set_params(), semi_supervised.SelfTrainingClassifier.decision_function(), semi_supervised.SelfTrainingClassifier.fit(), semi_supervised.SelfTrainingClassifier.get_params(), semi_supervised.SelfTrainingClassifier.predict(), semi_supervised.SelfTrainingClassifier.predict_log_proba(), semi_supervised.SelfTrainingClassifier.predict_proba(), semi_supervised.SelfTrainingClassifier.score(), semi_supervised.SelfTrainingClassifier.set_params(), tree.DecisionTreeClassifier.cost_complexity_pruning_path(), tree.DecisionTreeClassifier.decision_path(), tree.DecisionTreeClassifier.feature_importances_(), tree.DecisionTreeClassifier.get_n_leaves(), tree.DecisionTreeClassifier.predict_log_proba(), tree.DecisionTreeClassifier.predict_proba(), tree.DecisionTreeRegressor.cost_complexity_pruning_path(), tree.DecisionTreeRegressor.decision_path(), tree.DecisionTreeRegressor.feature_importances_(), tree.DecisionTreeRegressor.get_n_leaves(), tree.ExtraTreeClassifier.cost_complexity_pruning_path(), tree.ExtraTreeClassifier.feature_importances_(), tree.ExtraTreeClassifier.predict_log_proba(), tree.ExtraTreeRegressor.cost_complexity_pruning_path(), tree.ExtraTreeRegressor.feature_importances_(), sklearn.utils.register_parallel_backend(), sklearn.utils.estimator_checks.check_estimator(), sklearn.utils.estimator_checks.parametrize_with_checks(), utils.estimator_checks.parametrize_with_checks(), sklearn.utils.extmath.randomized_range_finder(), sklearn.utils.graph.single_source_shortest_path_length(), utils.graph.single_source_shortest_path_length(), sklearn.utils.graph_shortest_path.graph_shortest_path(), utils.graph_shortest_path.graph_shortest_path(), sklearn.utils.metaestimators.if_delegate_has_method(), utils.metaestimators.if_delegate_has_method(), sklearn.utils.random.sample_without_replacement(), utils.random.sample_without_replacement(), sklearn.utils.sparsefuncs.incr_mean_variance_axis(), sklearn.utils.sparsefuncs.inplace_column_scale(), sklearn.utils.sparsefuncs.inplace_csr_column_scale(), sklearn.utils.sparsefuncs.inplace_row_scale(), sklearn.utils.sparsefuncs.inplace_swap_column(), sklearn.utils.sparsefuncs.inplace_swap_row(), sklearn.utils.sparsefuncs.mean_variance_axis(), utils.sparsefuncs.incr_mean_variance_axis(), utils.sparsefuncs.inplace_csr_column_scale(), sklearn.utils.sparsefuncs_fast.inplace_csr_row_normalize_l1(), sklearn.utils.sparsefuncs_fast.inplace_csr_row_normalize_l2(), utils.sparsefuncs_fast.inplace_csr_row_normalize_l1(), utils.sparsefuncs_fast.inplace_csr_row_normalize_l2(), sklearn.utils.validation.check_is_fitted(), sklearn.utils.validation.check_symmetric(), sklearn.utils.validation.has_fit_parameter().

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