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A demo of the Spectral Biclustering algorithm —...
This example demonstrates how to generate a checkerboard dataset and bicluster it using the SpectralBiclustering algorithm. The spectral biclustering algorithm is specifically designed to cluster d...scikit-learn.org/stable/auto_examples/bicluster/plot_spectral_biclustering.html -
Early stopping in Gradient Boosting — scikit-le...
Gradient Boosting is an ensemble technique that combines multiple weak learners, typically decision trees, to create a robust and powerful predictive model. It does so in an iterative fashion, wher...scikit-learn.org/stable/auto_examples/ensemble/plot_gradient_boosting_early_stopping.html -
Sparse inverse covariance estimation — scikit-l...
Using the GraphicalLasso estimator to learn a covariance and sparse precision from a small number of samples. To estimate a probabilistic model (e.g. a Gaussian model), estimating the precision mat...scikit-learn.org/stable/auto_examples/covariance/plot_sparse_cov.html -
Agglomerative clustering with different metrics...
Demonstrates the effect of different metrics on the hierarchical clustering. The example is engineered to show the effect of the choice of different metrics. It is applied to waveforms, which can b...scikit-learn.org/stable/auto_examples/cluster/plot_agglomerative_clustering_metrics.html -
Two-class AdaBoost — scikit-learn 1.7.2 documen...
This example fits an AdaBoosted decision stump on a non-linearly separable classification dataset composed of two “Gaussian quantiles” clusters (see sklearn.datasets.make_gaussian_quantiles) and pl...scikit-learn.org/stable/auto_examples/ensemble/plot_adaboost_twoclass.html -
SVM Tie Breaking Example — scikit-learn 1.7.2 d...
Tie breaking is costly if decision_function_shape='ovr', and therefore it is not enabled by default. This example illustrates the effect of the break_ties parameter for a multiclass classification ...scikit-learn.org/stable/auto_examples/svm/plot_svm_tie_breaking.html -
Gaussian Process for Machine Learning — scikit-...
Examples concerning the sklearn.gaussian_process module. Ability of Gaussian process regression (GPR) to estimate data noise-level Comparison of kernel ridge and Gaussian process regression Forecas...scikit-learn.org/stable/auto_examples/gaussian_process/index.html -
5.1. Partial Dependence and Individual Conditio...
Partial dependence plots (PDP) and individual conditional expectation (ICE) plots can be used to visualize and analyze interaction between the target response 1 and a set of input features of inter...scikit-learn.org/stable/modules/partial_dependence.html -
Normal, Ledoit-Wolf and OAS Linear Discriminant...
This example illustrates how the Ledoit-Wolf and Oracle Approximating Shrinkage (OAS) estimators of covariance can improve classification. Total running time of the script:(0 minutes 7.730 seconds)...scikit-learn.org/stable/auto_examples/classification/plot_lda.html -
__sklearn_is_fitted__ as Developer API — scikit...
The__sklearn_is_fitted__ method is a convention used in scikit-learn for checking whether an estimator object has been fitted or not. This method is typically implemented in custom estimator classe...scikit-learn.org/stable/auto_examples/developing_estimators/sklearn_is_fitted.html