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Manifold Learning methods on a severed sphere —...
An application of the different Manifold learning techniques on a spherical data-set. Here one can see the use of dimensionality reduction in order to gain some intuition regarding the manifold lea...scikit-learn.org/stable/auto_examples/manifold/plot_manifold_sphere.html -
Comparison of F-test and mutual information — s...
This example illustrates the differences between univariate F-test statistics and mutual information. We consider 3 features x_1, x_2, x_3 distributed uniformly over [0, 1], the target depends on t...scikit-learn.org/stable/auto_examples/feature_selection/plot_f_test_vs_mi.html -
Face completion with a multi-output estimators ...
This example shows the use of multi-output estimator to complete images. The goal is to predict the lower half of a face given its upper half. The first column of images shows true faces. The next ...scikit-learn.org/stable/auto_examples/miscellaneous/plot_multioutput_face_completion.html -
SGD: Maximum margin separating hyperplane — sci...
Plot the maximum margin separating hyperplane within a two-class separable dataset using a linear Support Vector Machines classifier trained using SGD. Total running time of the script:(0 minutes 0...scikit-learn.org/stable/auto_examples/linear_model/plot_sgd_separating_hyperplane.html -
7.1. Pipelines and composite estimators — sciki...
To build a composite estimator, transformers are usually combined with other transformers or with predictors(such as classifiers or regressors). The most common tool used for composing estimators i...scikit-learn.org/stable/modules/compose.html -
SVM: Separating hyperplane for unbalanced class...
Find the optimal separating hyperplane using an SVC for classes that are unbalanced. We first find the separating plane with a plain SVC and then plot (dashed) the separating hyperplane with automa...scikit-learn.org/stable/auto_examples/svm/plot_separating_hyperplane_unbalanced.html -
scikit-learn: machine learning in Python — scik...
Skip to main content Back to top Ctrl + K scikit-learn Machine Learning in Python Getting Started Release Highlights ...scikit-learn.org/stable/index.html -
Demo of affinity propagation clustering algorit...
Reference: Brendan J. Frey and Delbert Dueck, “Clustering by Passing Messages Between Data Points”, Science Feb. 2007 Generate sample data: Compute Affinity Propagation: Plot result: Total running ...scikit-learn.org/stable/auto_examples/cluster/plot_affinity_propagation.html -
2.1. Gaussian mixture models — scikit-learn 1.8...
sklearn.mixture is a package which enables one to learn Gaussian Mixture Models (diagonal, spherical, tied and full covariance matrices supported), sample them, and estimate them from data. Facilit...scikit-learn.org/stable/modules/mixture.html -
11. Common pitfalls and recommended practices —...
The purpose of this chapter is to illustrate some common pitfalls and anti-patterns that occur when using scikit-learn. It provides examples of what not to do, along with a corresponding correct ex...scikit-learn.org/stable/common_pitfalls.html