Examples¶
General examples¶
General-purpose and introductory examples for the scikit.
- Classification of text documents using sparse features
- Pipeline Anova SVM
- Parameter estimation using grid search with a nested cross-validation
- Sample pipeline for text feature extraction and evaluation
- Logistic Regression
- Classification of text documents: using a MLComp dataset
- Gaussian Naive Bayes
- Plot classification probability
- Confusion matrix
- Recognizing hand-written digits
- Univariate Feature Selection
- Linear Discriminant Analysis & Quadratic Discriminant Analysis with confidence
- Linear and Quadratic Discriminant Analysis with confidence ellipsoid
- Nearest Neighbors
- k-Nearest Neighbors regression
- Test with permutations the significance of a classification score
- PLS Partial Least Squares
- Precision-Recall
- Recursive feature elimination
- Recursive feature elimination with cross-validation
- Receiver operating characteristic (ROC)
- Receiver operating characteristic (ROC) with cross validation
- Train error vs Test error
Examples based on real world datasets¶
Applications to real world problems with some medium sized datasets or interactive user interface.
Clustering¶
Examples concerning the scikits.learn.cluster package.
- A demo of K-Means clustering on the handwritten digits data
- Demo of affinity propagation clustering algorithm
- Feature agglomeration vs. univariate selection
- Segmenting the picture of Lena in regions
- A demo of structured Ward hierarchical clustering on Lena image
- A demo of the mean-shift clustering algorithm
- Spectral clustering for image segmentation
- Hierarchical clustering: structured vs unstructured ward
Covariance estimation¶
Examples concerning the scikits.learn.covariance package.
Decomposition¶
Examples concerning the scikits.learn.decomposition package.
Gaussian Process for Machine Learning¶
Examples concerning the scikits.learn.gaussian_process package.
Generalized Linear Models¶
Examples concerning the scikits.learn.linear_model package.
- Lasso regression example
- Lasso on dense and sparse data
- Lasso parameter estimation with path and cross-validation
- Automatic Relevance Determination Regression (ARD)
- Bayesian Ridge Regression
- Lasso and Elastic Net
- Lasso path using LARS
- Cross validated Lasso path with coordinate descent
- Path with L1- Logistic Regression
- Ordinary Least Squares
- Polynomial interpolation
- Plot multi-class SGD on the iris dataset
- SGD: Convex Loss Functions
- Ordinary Least Squares with SGD
- SGD: Penalties
- SGD: Maximum margin separating hyperplane
- SGD: Separating hyperplane with weighted classes
- SGD: Weighted samples
Gaussian Mixture Models¶
Examples concerning the scikits.learn.mixture package.
Support Vector Machines¶
Examples concerning the scikits.learn.svm package.
- SVM with custom kernel
- Plot different SVM classifiers in the iris dataset
- One-class SVM with non-linear kernel (RBF)
- SVM: Maximum margin separating hyperplane
- SVM: Separating hyperplane for unbalanced classes
- SVM-Anova: SVM with univariate feature selection
- Non-linear SVM
- Support Vector Regression (SVR) using linear and non-linear kernels
- SVM: Weighted samples