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Examples — scikit-learn 1.9.1 documentation

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Examples

Examples#

This is the gallery of examples that showcase how scikit-learn can be used. Some examples demonstrate the use of the API in general and some demonstrate specific applications in tutorial form. Also check out our user guide for more detailed illustrations.

Release Highlights#

These examples illustrate the main features of the releases of scikit-learn.

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Release Highlights for scikit-learn 1.9

Release Highlights for scikit-learn 1.9

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Release Highlights for scikit-learn 1.8

Release Highlights for scikit-learn 1.8

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Release Highlights for scikit-learn 1.7

Release Highlights for scikit-learn 1.7

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Release Highlights for scikit-learn 1.6

Release Highlights for scikit-learn 1.6

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Release Highlights for scikit-learn 1.5

Release Highlights for scikit-learn 1.5

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Release Highlights for scikit-learn 1.4

Release Highlights for scikit-learn 1.4

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Release Highlights for scikit-learn 1.3

Release Highlights for scikit-learn 1.3

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Release Highlights for scikit-learn 1.2

Release Highlights for scikit-learn 1.2

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Release Highlights for scikit-learn 1.1

Release Highlights for scikit-learn 1.1

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Release Highlights for scikit-learn 1.0

Release Highlights for scikit-learn 1.0

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Release Highlights for scikit-learn 0.24

Release Highlights for scikit-learn 0.24

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Release Highlights for scikit-learn 0.23

Release Highlights for scikit-learn 0.23

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Release Highlights for scikit-learn 0.22

Release Highlights for scikit-learn 0.22

Biclustering#

Examples concerning biclustering techniques.

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A demo of the Spectral Biclustering algorithm

A demo of the Spectral Biclustering algorithm

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A demo of the Spectral Co-Clustering algorithm

A demo of the Spectral Co-Clustering algorithm

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Biclustering documents with the Spectral Co-clustering algorithm

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

Comparison of Calibration of Classifiers

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Probability Calibration curves

Probability Calibration curves

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Probability Calibration for 3-class classification

Probability Calibration for 3-class classification

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Probability calibration of classifiers

Probability calibration of classifiers

Callbacks#

Examples related to the callback API of scikit-learn.

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Analysis of the convergence of penalized logistic regression models

Analysis of the convergence of penalized logistic regression models

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Supporting callbacks in third party estimators

Supporting callbacks in third party estimators

Classification#

General examples about classification algorithms.

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Classifier comparison

Classifier comparison

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Linear and Quadratic Discriminant Analysis with covariance ellipsoid

Linear and Quadratic Discriminant Analysis with covariance ellipsoid

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Normal, Ledoit-Wolf and OAS Linear Discriminant Analysis for classification

Normal, Ledoit-Wolf and OAS Linear Discriminant Analysis for classification

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Plot classification probability

Plot classification probability

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Recognizing hand-written digits

Recognizing hand-written digits

Clustering#

Examples concerning the sklearn.cluster module.

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

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

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

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

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A demo of the mean-shift clustering algorithm

A demo of the mean-shift clustering algorithm

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Adjustment for chance in clustering performance evaluation

Adjustment for chance in clustering performance evaluation

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Agglomerative clustering with different metrics

Agglomerative clustering with different metrics

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An example of K-Means++ initialization

An example of K-Means++ initialization

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Bisecting K-Means and Regular K-Means Performance Comparison

Bisecting K-Means and Regular K-Means Performance Comparison

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Compare BIRCH and MiniBatchKMeans

Compare BIRCH and MiniBatchKMeans

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Comparing different clustering algorithms on toy datasets

Comparing different clustering algorithms on toy datasets

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Comparing different hierarchical linkage methods on toy datasets

Comparing different hierarchical linkage methods on toy datasets

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Comparison of the K-Means and MiniBatchKMeans clustering algorithms

Comparison of the K-Means and MiniBatchKMeans clustering algorithms

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Demo of DBSCAN clustering algorithm

Demo of DBSCAN clustering algorithm

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Demo of HDBSCAN clustering algorithm

Demo of HDBSCAN clustering algorithm

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Demo of OPTICS clustering algorithm

Demo of OPTICS clustering algorithm

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Demo of affinity propagation clustering algorithm

Demo of affinity propagation clustering algorithm

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Demonstration of k-means assumptions

Demonstration of k-means assumptions

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Empirical evaluation of the impact of k-means initialization

Empirical evaluation of the impact of k-means initialization

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Feature agglomeration

Feature agglomeration

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Feature agglomeration vs. univariate selection

Feature agglomeration vs. univariate selection

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Hierarchical clustering with and without structure

Hierarchical clustering with and without structure

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Inductive Clustering

Inductive Clustering

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Online learning of a dictionary of parts of faces

Online learning of a dictionary of parts of faces

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Plot Hierarchical Clustering Dendrogram

Plot Hierarchical Clustering Dendrogram

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Segmenting the picture of greek coins in regions

Segmenting the picture of greek coins in regions

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Selecting the number of clusters with silhouette analysis on KMeans clustering

Selecting the number of clusters with silhouette analysis on KMeans clustering

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Spectral clustering for image segmentation

Spectral clustering for image segmentation

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Various Agglomerative Clustering on a 2D embedding of digits

Various Agglomerative Clustering on a 2D embedding of digits

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Vector Quantization Example

Vector Quantization Example

Covariance estimation#

Examples concerning the sklearn.covariance module.

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Ledoit-Wolf vs OAS estimation

Ledoit-Wolf vs OAS estimation

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Robust covariance estimation and Mahalanobis distances relevance

Robust covariance estimation and Mahalanobis distances relevance

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Robust vs Empirical covariance estimate

Robust vs Empirical covariance estimate

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Shrinkage covariance estimation: LedoitWolf vs OAS and max-likelihood

Shrinkage covariance estimation: LedoitWolf vs OAS and max-likelihood

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Sparse inverse covariance estimation

Sparse inverse covariance estimation

Cross decomposition#

Examples concerning the sklearn.cross_decomposition module.

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Compare cross decomposition methods

Compare cross decomposition methods

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Principal Component Regression vs Partial Least Squares Regression

Principal Component Regression vs Partial Least Squares Regression

Dataset examples#

Examples concerning the sklearn.datasets module.

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Plot randomly generated multilabel dataset

Plot randomly generated multilabel dataset

Decision Trees#

Examples concerning the sklearn.tree module.

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Decision Tree Regression

Decision Tree Regression

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Plot the decision surface of decision trees trained on the iris dataset

Plot the decision surface of decision trees trained on the iris dataset

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Post pruning decision trees with cost complexity pruning

Post pruning decision trees with cost complexity pruning

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Understanding the decision tree structure

Understanding the decision tree structure

Decomposition#

Examples concerning the sklearn.decomposition module.

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Blind source separation using FastICA

Blind source separation using FastICA

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Comparison of LDA and PCA 2D projection of Iris dataset

Comparison of LDA and PCA 2D projection of Iris dataset

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Faces dataset decompositions

Faces dataset decompositions

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Factor Analysis (with rotation) to visualize patterns

Factor Analysis (with rotation) to visualize patterns

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FastICA on 2D point clouds

FastICA on 2D point clouds

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Image denoising using dictionary learning

Image denoising using dictionary learning

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Incremental PCA

Incremental PCA

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Kernel PCA

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Model selection with Probabilistic PCA and Factor Analysis (FA)

Model selection with Probabilistic PCA and Factor Analysis (FA)

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Principal Component Analysis (PCA) on Iris Dataset

Principal Component Analysis (PCA) on Iris Dataset

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Sparse coding with a precomputed dictionary

Sparse coding with a precomputed dictionary

Developing Estimators#

Examples concerning the development of Custom Estimator.

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__sklearn_is_fitted__ as Developer API

__sklearn_is_fitted__ as Developer API

Ensemble methods#

Examples concerning the sklearn.ensemble module.

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Categorical Feature Support in Gradient Boosting

Categorical Feature Support in Gradient Boosting

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Combine predictors using stacking

Combine predictors using stacking

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Comparing Random Forests and Histogram Gradient Boosting models

Comparing Random Forests and Histogram Gradient Boosting models

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Comparing random forests and the multi-output meta estimator

Comparing random forests and the multi-output meta estimator

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Decision Tree Regression with AdaBoost

Decision Tree Regression with AdaBoost

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Early stopping in Gradient Boosting

Early stopping in Gradient Boosting

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Feature importances with a forest of trees

Feature importances with a forest of trees

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Feature transformations with ensembles of trees

Feature transformations with ensembles of trees

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Features in Histogram Gradient Boosting Trees

Features in Histogram Gradient Boosting Trees

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Gradient Boosting Out-of-Bag estimates

Gradient Boosting Out-of-Bag estimates

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Gradient Boosting regression

Gradient Boosting regression

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Gradient Boosting regularization

Gradient Boosting regularization

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Hashing feature transformation using Totally Random Trees

Hashing feature transformation using Totally Random Trees

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IsolationForest example

IsolationForest example

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Monotonic Constraints

Monotonic Constraints

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Multi-class AdaBoosted Decision Trees

Multi-class AdaBoosted Decision Trees

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OOB Errors for Random Forests

OOB Errors for Random Forests

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Plot individual and voting regression predictions

Plot individual and voting regression predictions

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

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

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Prediction Intervals for Gradient Boosting Regression

Prediction Intervals for Gradient Boosting Regression

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Single estimator versus bagging: bias-variance decomposition

Single estimator versus bagging: bias-variance decomposition

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Two-class AdaBoost

Two-class AdaBoost

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Visualizing the probabilistic predictions of a VotingClassifier

Visualizing the probabilistic predictions of a VotingClassifier

Examples based on real world datasets#

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

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Compressive sensing: tomography reconstruction with L1 prior (Lasso)

Compressive sensing: tomography reconstruction with L1 prior (Lasso)

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Faces recognition example using eigenfaces and kernel approximation

Faces recognition example using eigenfaces and kernel approximation

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Image denoising using kernel PCA

Image denoising using kernel PCA

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Lagged features for time series forecasting

Lagged features for time series forecasting

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Model Complexity Influence

Model Complexity Influence

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Out-of-core classification of text documents

Out-of-core classification of text documents

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Outlier detection on a real data set

Outlier detection on a real data set

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Prediction Latency

Prediction Latency

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Species distribution modeling

Species distribution modeling

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Time-related feature engineering

Time-related feature engineering

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

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

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Visualizing the stock market structure

Visualizing the stock market structure

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Wikipedia principal eigenvector

Wikipedia principal eigenvector

Feature Selection#

Examples concerning the sklearn.feature_selection module.

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Comparison of F-test and mutual information

Comparison of F-test and mutual information

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Model-based and sequential feature selection

Model-based and sequential feature selection

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Pipeline ANOVA SVM

Pipeline ANOVA SVM

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Recursive feature elimination

Recursive feature elimination

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Recursive feature elimination with cross-validation

Recursive feature elimination with cross-validation

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Univariate Feature Selection

Univariate Feature Selection

Frozen Estimators#

Examples concerning the sklearn.frozen module.

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Examples of Using FrozenEstimator

Examples of Using FrozenEstimator

Gaussian Mixture Models#

Examples concerning the sklearn.mixture module.

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Concentration Prior Type Analysis of Variation Bayesian Gaussian Mixture

Concentration Prior Type Analysis of Variation Bayesian Gaussian Mixture

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Density Estimation for a Gaussian mixture

Density Estimation for a Gaussian mixture

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GMM Initialization Methods

GMM Initialization Methods

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GMM covariances

GMM covariances

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Gaussian Mixture Model Ellipsoids

Gaussian Mixture Model Ellipsoids

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Gaussian Mixture Model Selection

Gaussian Mixture Model Selection

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Gaussian Mixture Model Sine Curve

Gaussian Mixture Model Sine Curve

Gaussian Process for Machine Learning#

Examples concerning the sklearn.gaussian_process module.

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Ability of Gaussian process regression (GPR) to estimate data noise-level

Ability of Gaussian process regression (GPR) to estimate data noise-level

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Comparison of kernel ridge and Gaussian process regression

Comparison of kernel ridge and Gaussian process regression

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Forecasting of CO2 level on Mona Loa dataset using Gaussian process regression (GPR)

Forecasting of CO2 level on Mona Loa dataset using Gaussian process regression (GPR)

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Gaussian Processes regression: basic introductory example

Gaussian Processes regression: basic introductory example

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Gaussian process classification (GPC) on iris dataset

Gaussian process classification (GPC) on iris dataset

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Gaussian processes on discrete data structures

Gaussian processes on discrete data structures

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Illustration of Gaussian process classification (GPC) on the XOR dataset

Illustration of Gaussian process classification (GPC) on the XOR dataset

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Illustration of prior and posterior Gaussian process for different kernels

Illustration of prior and posterior Gaussian process for different kernels

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Iso-probability lines for Gaussian Processes classification (GPC)

Iso-probability lines for Gaussian Processes classification (GPC)

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Probabilistic predictions with Gaussian process classification (GPC)

Probabilistic predictions with Gaussian process classification (GPC)

Generalized Linear Models#

Examples concerning the sklearn.linear_model module.

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Comparing Linear Bayesian Regressors

Comparing Linear Bayesian Regressors

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Curve Fitting with Bayesian Ridge Regression

Curve Fitting with Bayesian Ridge Regression

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Decision Boundaries of Multinomial and One-vs-Rest Logistic Regression

Decision Boundaries of Multinomial and One-vs-Rest Logistic Regression

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Early stopping of Stochastic Gradient Descent

Early stopping of Stochastic Gradient Descent

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Fitting an Elastic Net with a precomputed Gram Matrix and Weighted Samples

Fitting an Elastic Net with a precomputed Gram Matrix and Weighted Samples

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HuberRegressor vs Ridge on dataset with strong outliers

HuberRegressor vs Ridge on dataset with strong outliers

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Joint feature selection with multi-task Lasso

Joint feature selection with multi-task Lasso

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L1 Penalty and Sparsity in Logistic Regression

L1 Penalty and Sparsity in Logistic Regression

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L1-based models for Sparse Signals

L1-based models for Sparse Signals

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Lasso model selection via information criteria

Lasso model selection via information criteria

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Lasso model selection: AIC-BIC / cross-validation

Lasso model selection: AIC-BIC / cross-validation

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Lasso on dense and sparse data

Lasso on dense and sparse data

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Lasso, Lasso-LARS, and Elastic Net paths

Lasso, Lasso-LARS, and Elastic Net paths

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MNIST classification using multinomial logistic + L1

MNIST classification using multinomial logistic + L1

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Multiclass sparse logistic regression on 20newgroups

Multiclass sparse logistic regression on 20newgroups

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Non-negative least squares

Non-negative least squares

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One-Class SVM versus One-Class SVM using Stochastic Gradient Descent

One-Class SVM versus One-Class SVM using Stochastic Gradient Descent

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Ordinary Least Squares and Ridge Regression

Ordinary Least Squares and Ridge Regression

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Orthogonal Matching Pursuit

Orthogonal Matching Pursuit

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Plot Ridge coefficients as a function of the regularization

Plot Ridge coefficients as a function of the regularization

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Plot multi-class SGD on the iris dataset

Plot multi-class SGD on the iris dataset

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Poisson regression and non-normal loss

Poisson regression and non-normal loss

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Polynomial and Spline interpolation

Polynomial and Spline interpolation

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Quantile regression

Quantile regression

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Regularization path of L1- Logistic Regression

Regularization path of L1- Logistic Regression

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Ridge coefficients as a function of the L2 Regularization

Ridge coefficients as a function of the L2 Regularization

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Robust linear estimator fitting

Robust linear estimator fitting

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Robust linear model estimation using RANSAC

Robust linear model estimation using RANSAC

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SGD: Maximum margin separating hyperplane

SGD: Maximum margin separating hyperplane

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SGD: Penalties

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SGD: Weighted samples

SGD: Weighted samples

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SGD: convex loss functions

SGD: convex loss functions

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Theil-Sen Regression

Theil-Sen Regression

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Tweedie regression on insurance claims

Tweedie regression on insurance claims

Inspection#

Examples related to the sklearn.inspection module.

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Advanced Plotting With Partial Dependence

Advanced Plotting With Partial Dependence

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Common pitfalls in the interpretation of coefficients of linear models

Common pitfalls in the interpretation of coefficients of linear models

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Failure of Machine Learning to infer causal effects

Failure of Machine Learning to infer causal effects

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Partial Dependence and Individual Conditional Expectation Plots

Partial Dependence and Individual Conditional Expectation Plots

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Permutation Importance vs Random Forest Feature Importance (MDI)

Permutation Importance vs Random Forest Feature Importance (MDI)

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Permutation Importance with Multicollinear or Correlated Features

Permutation Importance with Multicollinear or Correlated Features

Kernel Approximation#

Examples concerning the sklearn.kernel_approximation module.

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Scalable learning with polynomial kernel approximation

Scalable learning with polynomial kernel approximation

Manifold learning#

Examples concerning the sklearn.manifold module.

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Comparison of Manifold Learning methods

Comparison of Manifold Learning methods

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Manifold Learning methods on a severed sphere

Manifold Learning methods on a severed sphere

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Manifold learning on handwritten digits: Locally Linear Embedding, Isomap

Manifold learning on handwritten digits: Locally Linear Embedding, Isomap...

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Multi-dimensional scaling

Multi-dimensional scaling

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Swiss Roll And Swiss-Hole Reduction

Swiss Roll And Swiss-Hole Reduction

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

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

Miscellaneous#

Miscellaneous and introductory examples for scikit-learn.

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Comparing anomaly detection algorithms for outlier detection on toy datasets

Comparing anomaly detection algorithms for outlier detection on toy datasets

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Comparison of kernel ridge regression and SVR

Comparison of kernel ridge regression and SVR

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Developing Estimators Compliant with Metadata Routing

Developing Estimators Compliant with Metadata Routing

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Displaying Pipelines

Displaying Pipelines

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Displaying estimators and complex pipelines

Displaying estimators and complex pipelines

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Evaluation of outlier detection estimators

Evaluation of outlier detection estimators

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Explicit feature map approximation for RBF kernels

Explicit feature map approximation for RBF kernels

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Face completion with a multi-output estimators

Face completion with a multi-output estimators

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Introducing the set_output API

Introducing the set_output API

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Isotonic Regression

Isotonic Regression

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Multilabel classification

Multilabel classification

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ROC Curve with Visualization API

ROC Curve with Visualization API

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The Johnson-Lindenstrauss bound for embedding with random projections

The Johnson-Lindenstrauss bound for embedding with random projections

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Visualizations with Display Objects

Visualizations with Display Objects

Missing Value Imputation#

Examples concerning the sklearn.impute module.

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Imputing missing values before building an estimator

Imputing missing values before building an estimator

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Imputing missing values with variants of IterativeImputer

Imputing missing values with variants of IterativeImputer

Model Selection#

Examples related to the sklearn.model_selection module.

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Balance model complexity and cross-validated score

Balance model complexity and cross-validated score

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Class Likelihood Ratios to measure classification performance

Class Likelihood Ratios to measure classification performance

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Comparing randomized search and grid search for hyperparameter estimation

Comparing randomized search and grid search for hyperparameter estimation

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Comparison between grid search and successive halving

Comparison between grid search and successive halving

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Custom refit strategy of a grid search with cross-validation

Custom refit strategy of a grid search with cross-validation

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Demonstration of multi-metric evaluation on cross_val_score and GridSearchCV

Demonstration of multi-metric evaluation on cross_val_score and GridSearchCV

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Detection error tradeoff (DET) curve

Detection error tradeoff (DET) curve

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Effect of model regularization on training and test error

Effect of model regularization on training and test error

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Evaluate the performance of a classifier with Confusion Matrix

Evaluate the performance of a classifier with Confusion Matrix

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Multiclass Receiver Operating Characteristic (ROC)

Multiclass Receiver Operating Characteristic (ROC)

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Nested versus non-nested cross-validation

Nested versus non-nested cross-validation

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Plotting Cross-Validated Predictions

Plotting Cross-Validated Predictions

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Plotting Learning Curves and Checking Models Scalability

Plotting Learning Curves and Checking Models' Scalability

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Post-hoc tuning the cut-off point of decision function

Post-hoc tuning the cut-off point of decision function

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Post-tuning the decision threshold for cost-sensitive learning

Post-tuning the decision threshold for cost-sensitive learning

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Precision-Recall

Precision-Recall

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Receiver Operating Characteristic (ROC) with cross validation

Receiver Operating Characteristic (ROC) with cross validation

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Sample pipeline for text feature extraction and evaluation

Sample pipeline for text feature extraction and evaluation

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Statistical comparison of models using grid search

Statistical comparison of models using grid search

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Successive Halving Iterations

Successive Halving Iterations

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Test with permutations the significance of a classification score

Test with permutations the significance of a classification score

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Underfitting vs. Overfitting

Underfitting vs. Overfitting

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Visualizing cross-validation behavior in scikit-learn

Visualizing cross-validation behavior in scikit-learn

Multiclass methods#

Examples concerning the sklearn.multiclass module.

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Overview of multiclass training meta-estimators

Overview of multiclass training meta-estimators

Multioutput methods#

Examples concerning the sklearn.multioutput module.

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Multilabel classification using a classifier chain

Multilabel classification using a classifier chain

Nearest Neighbors#

Examples concerning the sklearn.neighbors module.

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Approximate nearest neighbors in TSNE

Approximate nearest neighbors in TSNE

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Caching nearest neighbors

Caching nearest neighbors

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Comparing Nearest Neighbors with and without Neighborhood Components Analysis

Comparing Nearest Neighbors with and without Neighborhood Components Analysis

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Dimensionality Reduction with Neighborhood Components Analysis

Dimensionality Reduction with Neighborhood Components Analysis

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Kernel Density Estimate of Species Distributions

Kernel Density Estimate of Species Distributions

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Kernel Density Estimation

Kernel Density Estimation

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Nearest Centroid Classification

Nearest Centroid Classification

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Nearest Neighbors Classification

Nearest Neighbors Classification

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Nearest Neighbors regression

Nearest Neighbors regression

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Neighborhood Components Analysis Illustration

Neighborhood Components Analysis Illustration

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Novelty detection with Local Outlier Factor (LOF)

Novelty detection with Local Outlier Factor (LOF)

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Outlier detection with Local Outlier Factor (LOF)

Outlier detection with Local Outlier Factor (LOF)

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Simple 1D Kernel Density Estimation

Simple 1D Kernel Density Estimation

Neural Networks#

Examples concerning the sklearn.neural_network module.

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Compare Stochastic learning strategies for MLPClassifier

Compare Stochastic learning strategies for MLPClassifier

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Restricted Boltzmann Machine features for digit classification

Restricted Boltzmann Machine features for digit classification

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Varying regularization in Multi-layer Perceptron

Varying regularization in Multi-layer Perceptron

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Visualization of MLP weights on MNIST

Visualization of MLP weights on MNIST

Pipelines and composite estimators#

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

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Column Transformer with Heterogeneous Data Sources

Column Transformer with Heterogeneous Data Sources

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Column Transformer with Mixed Types

Column Transformer with Mixed Types

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Concatenating multiple feature extraction methods

Concatenating multiple feature extraction methods

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Effect of transforming the targets in regression model

Effect of transforming the targets in regression model

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Pipelining: chaining a PCA and a logistic regression

Pipelining: chaining a PCA and a logistic regression

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Selecting dimensionality reduction with Pipeline and GridSearchCV

Selecting dimensionality reduction with Pipeline and GridSearchCV

Preprocessing#

Examples concerning the sklearn.preprocessing module.

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Compare the effect of different scalers on data with outliers

Compare the effect of different scalers on data with outliers

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Comparing Target Encoder with Other Encoders

Comparing Target Encoder with Other Encoders

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Demonstrating the different strategies of KBinsDiscretizer

Demonstrating the different strategies of KBinsDiscretizer

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Feature discretization

Feature discretization

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Importance of Feature Scaling

Importance of Feature Scaling

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Map data to a normal distribution

Map data to a normal distribution

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Target Encoders Internal Cross fitting

Target Encoder's Internal Cross fitting

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Using KBinsDiscretizer to discretize continuous features

Using KBinsDiscretizer to discretize continuous features

Semi Supervised Classification#

Examples concerning the sklearn.semi_supervised module.

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Decision boundary of semi-supervised classifiers versus SVM on the Iris dataset

Decision boundary of semi-supervised classifiers versus SVM on the Iris dataset

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Effect of varying threshold for self-training

Effect of varying threshold for self-training

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Label Propagation circles: Learning a complex structure

Label Propagation circles: Learning a complex structure

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Label Propagation digits: Active learning

Label Propagation digits: Active learning

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Label Propagation digits: Demonstrating performance

Label Propagation digits: Demonstrating performance

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Semi-supervised Classification on a Text Dataset

Semi-supervised Classification on a Text Dataset

Support Vector Machines#

Examples concerning the sklearn.svm module.

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One-class SVM with non-linear kernel (RBF)

One-class SVM with non-linear kernel (RBF)

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Plot classification boundaries with different SVM Kernels

Plot classification boundaries with different SVM Kernels

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Plot different SVM classifiers in the iris dataset

Plot different SVM classifiers in the iris dataset

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Plot the support vectors in LinearSVC

Plot the support vectors in LinearSVC

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RBF SVM parameters

RBF SVM parameters

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SVM Margins Example

SVM Margins Example

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SVM Tie Breaking Example

SVM Tie Breaking Example

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SVM with custom kernel

SVM with custom kernel

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SVM-Anova: SVM with univariate feature selection

SVM-Anova: SVM with univariate feature selection

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SVM: Maximum margin separating hyperplane

SVM: Maximum margin separating hyperplane

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SVM: Separating hyperplane for unbalanced classes

SVM: Separating hyperplane for unbalanced classes

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SVM: Weighted samples

SVM: Weighted samples

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Scaling the regularization parameter for SVCs

Scaling the regularization parameter for SVCs

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Support Vector Regression (SVR) using linear and non-linear kernels

Support Vector Regression (SVR) using linear and non-linear kernels

Working with text documents#

Examples concerning the sklearn.feature_extraction.text module.

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Classification of text documents using sparse features

Classification of text documents using sparse features

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Clustering text documents using k-means

Clustering text documents using k-means

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FeatureHasher and DictVectorizer Comparison

FeatureHasher and DictVectorizer Comparison

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