scikit-learn Tutorials Sitemap
- Simple Handwritten Character Recognition Classifier
- Credit Card Holder Risk Prediction
- Working with Text Data
- Classifying Iris Using SVM
- Feature Extraction with Scikit-Learn
- Preprocessing Techniques in Scikit-Learn
- Imputation of Missing Values
- Kernel Approximation Techniques in Scikit-Learn
- Pairwise Metrics and Kernels in Scikit-Learn
- Transforming the Prediction Target
- Partial Dependence and Individual Conditional Expectation
- Permutation Feature Importance
- Pipelines and Composite Estimators
- Validation Curves: Plotting Scores to Evaluate Models
- Tuning Hyperparameters of an Estimator
- Evaluating Machine Learning Model Quality
- Machine Learning Cross-Validation with Python
- Density Estimation Using Kernel Density
- Decomposing Signals in Components
- Covariance Matrix Estimation with Scikit-Learn
- Feature Selection with Scikit-Learn
- Semi-Supervised Learning Algorithms
- Nonlinear Regression with Isotonic
- Neural Network Models
- Gaussian Mixture Models
- Manifold Learning with Scikit-Learn
- Unsupervised Clustering with K-Means
- Biclustering in Scikit-Learn
- Gaussian Process Regression and Classification
- Dimensional Reduction with PLS Algorithms
- Naive Bayes Example
- Decision Tree Classification with Scikit-Learn
- Ensemble Methods Exploration with Scikit-Learn
- Multiclass and Multioutput Algorithms
- Implementing Stochastic Gradient Descent
- Exploring Scikit-Learn SGD Classifiers
- Supervised Learning with Scikit-Learn
- Model Selection: Choosing Estimators and Their Parameters
- Kernel Ridge Regression
- Scikit-Learn Libsvm GUI
- Wikipedia PageRank with Randomized SVD
- Linear Models in Scikit-Learn
- Discriminant Analysis Classifiers Explained
- Exploring Scikit-Learn Datasets and Estimators
- Iris Flower Classification using Voting Classifier
- Class Probabilities with VotingClassifier
- Diabetes Prediction Using Voting Regressor
- Hierarchical Clustering with Connectivity Constraints
- Plotting Validation Curves
- Revealing Iris Dataset Structure via Factor Analysis
- Decision Tree Analysis
- Underfitting and Overfitting
- Multi-Output Decision Tree Regression
- Decision Tree Regression
- Transforming Target for Linear Regression
- Compressive Sensing Image Reconstruction
- Plot Topics Extraction with NMF Lda
- Scikit-Learn Elastic-Net Regression Model
- Categorical Data Transformation using TargetEncoder
- Theil-Sen Regression with Python Scikit-Learn
- Scaling Regularization Parameter for SVMs
- SVM Tie Breaking
- Swiss Roll and Swiss-Hole Reduction
- Support Vector Regression
- Feature Selection for SVC on Iris Dataset
- Successive Halving Iterations
- Comparison Between Grid Search and Successive Halving
- Spectral Biclustering Algorithm
- Spectral Co-Clustering Algorithm
- Sparse Inverse Covariance Estimation
- Multiclass Sparse Logistic Regression
- MNIST Multinomial Logistic Regression
- Species Distribution Modeling
- Kernel Density Estimate of Species Distributions
- Plot SGD Separating Hyperplane
- Plot Sgdocsvm vs Ocsvm
- Sparse Coding with Precomputed Dictionary
- Applying Regularization Techniques with SGD
- Early Stopping of Stochastic Gradient Descent
- Scikit-Learn Multi-Class SGD Classifier
- Comparing Online Solvers for Handwritten Digit Classification
- SVM: Maximum Margin Separating Hyperplane
- Using Set_output API
- Spectral Clustering for Image Segmentation
- Model-Based and Sequential Feature Selection
- Effect of Varying Threshold for Self-Training
- Semi-Supervised Text Classification
- Semi-Supervised Classifiers on the Iris Dataset
- SVM for Unbalanced Classes
- ROC with Cross Validation
- Scikit-Learn Visualization API
- Multiclass ROC Evaluation with Scikit-Learn
- Polynomial Kernel Approximation with Scikit-Learn
- Feature Scaling in Machine Learning
- Robust Covariance Estimation in Python
- Robust Linear Estimator Fitting
- Recursive Feature Elimination with Cross-Validation
- Ridge Regression for Linear Modeling
- Recursive Feature Elimination
- Digit Classification with RBM Features
- Multilabel Dataset Generation with Scikit-Learn
- Hyperparameter Optimization: Randomized Search vs Grid Search
- Plot Random Forest Regression Multioutput
- Hashing Feature Transformation
- Quantile Regression with Scikit-Learn
- Random Classification Dataset Plotting
- Precision-Recall Metric for Imbalanced Classification
- Permutation Test Score for Classification
- Constructing Scikit-Learn Pipelines
- Plot Pca vs Fa Model Selection
- Plot Pca vs Lda
- Plot PCR vs PLS
- Outlier Detection Using Scikit-Learn Algorithms
- Detecting Outliers in Wine Data
- Principal Components Analysis
- Sparse Signal Recovery with Orthogonal Matching Pursuit
- OPTICS Clustering Algorithm
- Text Classification Using Out-of-Core Learning
- Non-Negative Least Squares Regression
- Linear Regression Example
- Neighborhood Components Analysis
- Nearest Centroid Classification
- Nested Cross-Validation for Model Selection
- Plot Nca Classification
- Joint Feature Selection with Multi-Task Lasso
- Multi-Label Document Classification
- Gradient Boosting Monotonic Constraints
- Optimizing Model Hyperparameters with GridSearchCV
- Impute Missing Data
- Multi-Layer Perceptron Regularization
- Scikit-Learn MLPClassifier: Stochastic Learning Strategies
- Classify Handwritten Digits with MLP Classifier
- Manifold Learning on Spherical Data
- Map Data to a Normal Distribution
- Visualize High-Dimensional Data with MDS
- Mean-Shift Clustering Algorithm
- Comparing K-Means and MiniBatchKMeans
- Regularization Path of L1-Logistic Regression
- Logistic Regression Model
- Comparison of Covariance Estimators
- Robust Covariance Estimation and Mahalanobis Distances Relevance
- Plot Multinomial and One-vs-Rest Logistic Regression
- Outlier Detection with LOF
- Step-by-Step Logistic Regression
- Local Outlier Factor for Novelty Detection
- Hierarchical Clustering with Scikit-Learn
- Manifold Learning on Handwritten Digits
- Model Selection for Lasso Regression
- Discriminant Analysis Classification Algorithms
- Linear Discriminant Analysis for Classification
- Plotting Learning Curves
- Class Likelihood Ratios to Measure Classification Performance
- Sparse Signal Regression with L1-Based Models
- Lasso and Elastic Net
- Scikit-Learn Lasso Regression
- Lasso Model Selection
- Active Learning Withel Propagation
- Label Propagation Learning
- Empirical Evaluation of K-Means Initialization
- Clustering Analysis with Silhouette Method
- K-Means Clustering on Handwritten Digits
- Exploring K-Means Clustering Assumptions
- Explicit Feature Map Approximation for RBF Kernels
- Principal Component Analysis with Kernel PCA
- Plot Kernel Ridge Regression
- Anomaly Detection with Isolation Forest
- Nonparametric Isotonic Regression with Scikit-Learn
- Scikit-Learn Iterative Imputer
- Exploring Johnson-Lindenstrauss Lemma with Random Projections
- Simple 1D Kernel Density Estimation
- Logistic Regression Classifier on Iris Dataset
- SVM Classifier on Iris Dataset
- Decision Trees on Iris Dataset
- Iris Flower Classification with Scikit-learn
- Incremental Principal Component Analysis on Iris Dataset
- Blind Source Separation
- Independent Component Analysis with FastICA and PCA
- Demo of HDBSCAN Clustering Algorithm
- Plot Huber vs Ridge
- FeatureHasher and DictVectorizer Comparison
- Text Feature Extraction and Evaluation
- Plot Grid Search Digits
- Balance Model Complexity and Cross-Validated Score
- Gradient Boosting Regularization
- Early Stopping of Gradient Boosting
- Gradient Boosting Out-of-Bag Estimates
- Gaussian Processes on Discrete Data Structures
- Gaussian Process Regression: Kernels
- Gradient Boosting with Categorical Features
- Probabilistic Predictions with Gaussian Process Classification
- Plot GPR Co2
- Fit Gaussian Process Regression Model
- Nonlinear Predictive Modeling Using Gaussian Process
- Gaussian Process Classification on XOR Dataset
- Gaussian Process Classification
- Gaussian Process Classification on Iris Dataset
- Gaussian Mixture Model Initialization Methods
- Gaussian Mixture Model Selection
- Plot Forest Iris
- Gaussian Mixture Model Covariances
- Feature Importance with Random Forest
- Pixel Importances with Parallel Forest of Trees
- Concatenating Multiple Feature Extraction Methods
- Plot Forest Hist Grad Boosting Comparison
- Feature Transformations with Ensembles of Trees
- Building Machine Learning Pipelines with Scikit-Learn
- Univariate Feature Selection
- Vector Quantization with KBinsDiscretizer
- Face Recognition with Eigenfaces and SVMs
- Faces Dataset Decompositions
- Comparison of F-Test and Mutual Information
- Scikit-Learn Estimators and Pipelines
- Precompute Gram Matrix for ElasticNet
- Random Forest OOB Error Estimation
- Text Document Classification
- Creating Visualizations with Display Objects
- Agglomerative Clustering on Digits Dataset
- Plot Digits Pipe
- Feature Discretization for Classification
- Demonstrating KBinsDiscretizer Strategies
- Discretizing Continuous Features with KBinsDiscretizer
- Recognizing Hand-Written Digits
- Image Denoising with Kernel PCA
- Kernel Density Estimation
- Digit Dataset Analysis
- DBSCAN Clustering Algorithm
- Detection Error Tradeoff Curve
- Plot Dict Face Patches
- Feature Agglomeration for High-Dimensional Data
- Digits Classification using Scikit-Learn
- Plotting Predictions with Cross-Validation
- Cross-Validation on Digits Dataset
- SVM Classification Using Custom Kernel
- Cross-Validation with Linear Models
- Shrinkage Covariance Estimation
- Dimensionality Reduction with Pipeline and GridSearchCV
- Plot Concentration Prior
- Scikit-Learn Confusion Matrix
- Post Pruning Decision Trees
- Plot Compare GPR KRR
- Comparison of Calibration of Classifiers
- Compare Cross Decomposition Methods
- Column Transformer with Mixed Types
- Color Quantization Using K-Means
- Image Segmentation with Hierarchical Clustering
- Nearest Neighbors Classification
- Scikit-Learn Classifier Comparison
- Comparing Clustering Algorithms
- Exploring K-Means Clustering with Python
- Plotting Classification Probability
- Probability Calibration for 3-Class Classification
- Probability Calibration Curves
- Document Biclustering Using Spectral Co-Clustering Algorithm
- Comparing BIRCH and MiniBatchKMeans
- Bisecting K-Means and Regular K-Means Performance Comparison
- Caching Nearest Neighbors
- Comparing Linear Bayesian Regressors
- Curve Fitting with Bayesian Ridge Regression
- Bias-Variance Decomposition with Bagging
- Anomaly Detection Algorithms Comparison
- Hierarchical Clustering Dendrogram
- Data Scaling and Transformation
- Plot Agglomerative Clustering
- Agglomerative Clustering Metrics
- Affinity Propagation Clustering
- Discrete Versus Real AdaBoost
- Multi-Class AdaBoosted Decision Trees
- Boosted Decision Tree Regression
- AdaBoost Decision Stump Classification
- Approximate Nearest Neighbors in TSNE