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Examples¶

Examples based on real world datasets¶

Applications to real world problems with some medium sized datasets or interactive user interface.

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Outlier detection on a real data set¶

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Compressive sensing: tomography reconstruction with L1 prior (Lasso)¶

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Topic extraction with Non-negative Matrix Factorization and Latent Dirichlet Allocation¶

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Faces recognition example using eigenfaces and SVMs¶

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Model Complexity Influence¶

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Species distribution modeling¶

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Visualizing the stock market structure¶

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Wikipedia principal eigenvector¶

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Prediction Latency¶

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Out-of-core classification of text documents¶

Biclustering¶

Examples concerning the sklearn.cluster.bicluster module.

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A demo of the Spectral Co-Clustering algorithm¶

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A demo of the Spectral Biclustering algorithm¶

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Biclustering documents with the Spectral Co-clustering algorithm¶

Calibration¶

Examples illustrating the calibration of predicted probabilities of classifiers.

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Comparison of Calibration of Classifiers¶

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Probability Calibration curves¶

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Probability calibration of classifiers¶

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Probability Calibration for 3-class classification¶

Classification¶

General examples about classification algorithms.

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Recognizing hand-written digits¶

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Normal and Shrinkage Linear Discriminant Analysis for classification¶

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Plot classification probability¶

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Classifier comparison¶

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Linear and Quadratic Discriminant Analysis with covariance ellipsoid¶

Clustering¶

Examples concerning the sklearn.cluster module.

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Plot Hierarchical Clustering Dendrogram¶

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Feature agglomeration¶

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A demo of the mean-shift clustering algorithm¶

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Demonstration of k-means assumptions¶

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Online learning of a dictionary of parts of faces¶

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Vector Quantization Example¶

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Agglomerative clustering with and without structure¶

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Demo of affinity propagation clustering algorithm¶

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Segmenting the picture of greek coins in regions¶

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Various Agglomerative Clustering on a 2D embedding of digits¶

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K-means Clustering¶

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Spectral clustering for image segmentation¶

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A demo of structured Ward hierarchical clustering on an image of coins¶

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Demo of DBSCAN clustering algorithm¶

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Color Quantization using K-Means¶

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Hierarchical clustering: structured vs unstructured ward¶

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Agglomerative clustering with different metrics¶

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Inductive Clustering¶

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Demo of OPTICS clustering algorithm¶

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Compare BIRCH and MiniBatchKMeans¶

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Empirical evaluation of the impact of k-means initialization¶

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Adjustment for chance in clustering performance evaluation¶

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Comparison of the K-Means and MiniBatchKMeans clustering algorithms¶

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Feature agglomeration vs. univariate selection¶

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A demo of K-Means clustering on the handwritten digits data¶

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Comparing different hierarchical linkage methods on toy datasets¶

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Selecting the number of clusters with silhouette analysis on KMeans clustering¶

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Comparing different clustering algorithms on toy datasets¶

Pipelines and composite estimators¶

Examples of how to compose transformers and pipelines from other estimators. See the User Guide.

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Concatenating multiple feature extraction methods¶

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Pipelining: chaining a PCA and a logistic regression¶

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Column Transformer with Mixed Types¶

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Selecting dimensionality reduction with Pipeline and GridSearchCV¶

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Column Transformer with Heterogeneous Data Sources¶

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Effect of transforming the targets in regression model¶

Covariance estimation¶

Examples concerning the sklearn.covariance module.

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Ledoit-Wolf vs OAS estimation¶

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Sparse inverse covariance estimation¶

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Shrinkage covariance estimation: LedoitWolf vs OAS and max-likelihood¶

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Robust covariance estimation and Mahalanobis distances relevance¶

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Robust vs Empirical covariance estimate¶

Cross decomposition¶

Examples concerning the sklearn.cross_decomposition module.

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Compare cross decomposition methods¶

Dataset examples¶

Examples concerning the sklearn.datasets module.

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The Digit Dataset¶

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The Iris Dataset¶

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Plot randomly generated classification dataset¶

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Plot randomly generated multilabel dataset¶

Ensemble methods¶

Examples concerning the sklearn.ensemble module.

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Pixel importances with a parallel forest of trees¶

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Decision Tree Regression with AdaBoost¶

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Plot individual and voting regression predictions¶

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Feature importances with forests of trees¶

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IsolationForest example¶

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Plot the decision boundaries of a VotingClassifier¶

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Comparing random forests and the multi-output meta estimator¶

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Prediction Intervals for Gradient Boosting Regression¶

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Gradient Boosting regularization¶

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Plot class probabilities calculated by the VotingClassifier¶

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Gradient Boosting regression¶

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OOB Errors for Random Forests¶

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Two-class AdaBoost¶

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Hashing feature transformation using Totally Random Trees¶

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Multi-class AdaBoosted Decision Trees¶

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Discrete versus Real AdaBoost¶

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Combine predictors using stacking¶

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Early stopping of Gradient Boosting¶

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Feature transformations with ensembles of trees¶

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Gradient Boosting Out-of-Bag estimates¶

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Single estimator versus bagging: bias-variance decomposition¶

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Plot the decision surfaces of ensembles of trees on the iris dataset¶

Tutorial exercises¶

Exercises for the tutorials

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Digits Classification Exercise¶

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Cross-validation on Digits Dataset Exercise¶

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Cross-validation on diabetes Dataset Exercise¶

Feature Selection¶

Examples concerning the sklearn.feature_selection module.

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Recursive feature elimination¶

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Comparison of F-test and mutual information¶

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Pipeline Anova SVM¶

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Recursive feature elimination with cross-validation¶

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Feature selection using SelectFromModel and LassoCV¶

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Test with permutations the significance of a classification score¶

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Univariate Feature Selection¶

Missing Value Imputation¶

Examples concerning the sklearn.impute module.

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Imputing missing values with variants of IterativeImputer¶

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Imputing missing values before building an estimator¶

Inspection¶

Examples related to the sklearn.inspection module.

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Permutation Importance with Multicollinear or Correlated Features¶

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Permutation Importance vs Random Forest Feature Importance (MDI)¶

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Partial Dependence Plots¶

Generalized Linear Models¶

Examples concerning the sklearn.linear_model module.

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Lasso path using LARS¶

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Plot Ridge coefficients as a function of the regularization¶

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SGD: Maximum margin separating hyperplane¶

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SGD: convex loss functions¶

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Ordinary Least Squares and Ridge Regression Variance¶

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Plot Ridge coefficients as a function of the L2 regularization¶

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SGD: Penalties¶

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Regularization path of L1- Logistic Regression¶

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Polynomial interpolation¶

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Logistic function¶

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Logistic Regression 3-class Classifier¶

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SGD: Weighted samples¶

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Linear Regression Example¶

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Robust linear model estimation using RANSAC¶

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Sparsity Example: Fitting only features 1 and 2¶

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HuberRegressor vs Ridge on dataset with strong outliers¶

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Lasso on dense and sparse data¶

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Comparing various online solvers¶

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Joint feature selection with multi-task Lasso¶

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MNIST classfification using multinomial logistic + L1¶

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Plot multi-class SGD on the iris dataset¶

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Orthogonal Matching Pursuit¶

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Lasso and Elastic Net for Sparse Signals¶

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Curve Fitting with Bayesian Ridge Regression¶

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Theil-Sen Regression¶

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Plot multinomial and One-vs-Rest Logistic Regression¶

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Robust linear estimator fitting¶

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L1 Penalty and Sparsity in Logistic Regression¶

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Lasso and Elastic Net¶

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Automatic Relevance Determination Regression (ARD)¶

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Bayesian Ridge Regression¶

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Lasso model selection: Cross-Validation / AIC / BIC¶

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Multiclass sparse logisitic regression on newgroups20¶

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Early stopping of Stochastic Gradient Descent¶

Manifold learning¶

Examples concerning the sklearn.manifold module.

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Swiss Roll reduction with LLE¶

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Multi-dimensional scaling¶

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t-SNE: The effect of various perplexity values on the shape¶

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Comparison of Manifold Learning methods¶

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Manifold Learning methods on a severed sphere¶

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Manifold learning on handwritten digits: Locally Linear Embedding, Isomap…¶

Gaussian Mixture Models¶

Examples concerning the sklearn.mixture module.

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Density Estimation for a Gaussian mixture¶

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Gaussian Mixture Model Ellipsoids¶

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Gaussian Mixture Model Selection¶

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GMM covariances¶

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Gaussian Mixture Model Sine Curve¶

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Concentration Prior Type Analysis of Variation Bayesian Gaussian Mixture¶

Model Selection¶

Examples related to the sklearn.model_selection module.

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Plotting Cross-Validated Predictions¶

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Plotting Validation Curves¶

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Underfitting vs. Overfitting¶

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Parameter estimation using grid search with cross-validation¶

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Train error vs Test error¶

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Receiver Operating Characteristic (ROC) with cross validation¶

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Comparing randomized search and grid search for hyperparameter estimation¶

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Nested versus non-nested cross-validation¶

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Demonstration of multi-metric evaluation on cross_val_score and GridSearchCV¶

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Sample pipeline for text feature extraction and evaluation¶

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Balance model complexity and cross-validated score¶

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Confusion matrix¶

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Visualizing cross-validation behavior in scikit-learn¶

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Receiver Operating Characteristic (ROC)¶

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Precision-Recall¶

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Plotting Learning Curves¶

Multioutput methods¶

Examples concerning the sklearn.multioutput module.

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Classifier Chain¶

Neural Networks¶

Examples concerning the sklearn.neural_network module.

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Visualization of MLP weights on MNIST¶

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Restricted Boltzmann Machine features for digit classification¶

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Varying regularization in Multi-layer Perceptron¶

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Compare Stochastic learning strategies for MLPClassifier¶

Preprocessing¶

Examples concerning the sklearn.preprocessing module.

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Using FunctionTransformer to select columns¶

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Using KBinsDiscretizer to discretize continuous features¶

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Demonstrating the different strategies of KBinsDiscretizer¶

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Importance of Feature Scaling¶

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Map data to a normal distribution¶

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Feature discretization¶

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Compare the effect of different scalers on data with outliers¶

Semi Supervised Classification¶

Examples concerning the sklearn.semi_supervised module.

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Decision boundary of label propagation versus SVM on the Iris dataset¶

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Label Propagation learning a complex structure¶

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Label Propagation digits: Demonstrating performance¶

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Label Propagation digits active learning¶

Working with text documents¶

Examples concerning the sklearn.feature_extraction.text module.

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FeatureHasher and DictVectorizer Comparison¶

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Clustering text documents using k-means¶

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Classification of text documents using sparse features¶

Decision Trees¶

Examples concerning the sklearn.tree module.

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Decision Tree Regression¶

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Multi-output Decision Tree Regression¶

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Plot the decision surface of a decision tree on the iris dataset¶

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Post pruning decision trees with cost complexity pruning¶

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Understanding the decision tree structure¶

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