Gallery examples: Statistical comparison of models using grid search Post-hoc tuning the cut-off point of decision function Overview of multiclass training meta-estimators| scikit-learn
Gallery examples: FeatureHasher and DictVectorizer Comparison| scikit-learn
Gallery examples: Scalable learning with polynomial kernel approximation Compare the effect of different scalers on data with outliers Clustering text documents using k-means| scikit-learn
Gallery examples: Hashing feature transformation using Totally Random Trees Manifold learning on handwritten digits: Locally Linear Embedding, Isomap… Clustering text documents using k-means| scikit-learn
Gallery examples: Biclustering documents with the Spectral Co-clustering algorithm Compare BIRCH and MiniBatchKMeans Comparing different clustering algorithms on toy datasets Online learning of a d...| scikit-learn
M{array-like, sparse matrix} of shape (n_samples, n_features)Matrix to decompose.| scikit-learn
Gallery examples: Classifier comparison Multi-class AdaBoosted Decision Trees Two-class AdaBoost Plot the decision surfaces of ensembles of trees on the iris dataset Demonstration of multi-metric e...| scikit-learn
Gallery examples: Common pitfalls in the interpretation of coefficients of linear models| scikit-learn
Gallery examples: Bisecting K-Means and Regular K-Means Performance Comparison Demonstration of k-means assumptions A demo of K-Means clustering on the handwritten digits data Selecting the number ...| scikit-learn
Gallery examples: Image denoising using kernel PCA Faces recognition example using eigenfaces and SVMs A demo of K-Means clustering on the handwritten digits data Column Transformer with Heterogene...| scikit-learn
Gallery examples: Recursive feature elimination with cross-validation GMM covariances Visualizing cross-validation behavior in scikit-learn Test with permutations the significance of a classificati...| scikit-learn
Gallery examples: Feature agglomeration vs. univariate selection Comparing Random Forests and Histogram Gradient Boosting models Gradient Boosting Out-of-Bag estimates Visualizing cross-validation ...| scikit-learn
Gallery examples: Release Highlights for scikit-learn 1.4 Visualizing cross-validation behavior in scikit-learn| scikit-learn
Gallery examples: Plot classification probability Decision Boundaries of Multinomial and One-vs-Rest Logistic Regression Multiclass sparse logistic regression on 20newgroups Multilabel classificati...| scikit-learn
This guide demonstrates how metadata can be routed and passed between objects in scikit-learn. If you are developing a scikit-learn compatible estimator or meta-estimator, you can check our related...| scikit-learn
Gallery examples: Release Highlights for scikit-learn 1.5 Release Highlights for scikit-learn 1.3 Release Highlights for scikit-learn 1.1 Release Highlights for scikit-learn 1.0 Release Highlights ...| scikit-learn
Gallery examples: Release Highlights for scikit-learn 1.5 Release Highlights for scikit-learn 1.4 Release Highlights for scikit-learn 1.2 Release Highlights for scikit-learn 1.1 Release Highlights ...| scikit-learn
Gallery examples: Combine predictors using stacking L1-based models for Sparse Signals Lasso model selection: AIC-BIC / cross-validation Common pitfalls in the interpretation of coefficients of lin...| scikit-learn
The following are a set of methods intended for regression in which the target value is expected to be a linear combination of the features. In mathematical notation, if\hat{y} is the predicted val...| scikit-learn
Ensemble methods combine the predictions of several base estimators built with a given learning algorithm in order to improve generalizability / robustness over a single estimator. Two very famous ...| scikit-learn
Gallery examples: Release Highlights for scikit-learn 1.4 Release Highlights for scikit-learn 0.24 Feature agglomeration vs. univariate selection Shrinkage covariance estimation: LedoitWolf vs OAS ...| scikit-learn
Gallery examples: Release Highlights for scikit-learn 1.3 Model selection with Probabilistic PCA and Factor Analysis (FA) Lagged features for time series forecasting Imputing missing values before ...| scikit-learn
Gallery examples: Feature agglomeration vs. univariate selection Pipeline ANOVA SVM Recursive feature elimination Poisson regression and non-normal loss Permutation Importance vs Random Forest Feat...| scikit-learn