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4. Inspection — scikit-learn 1.6.1 documentation
Predictive performance is often the main goal of developing machine learning models. Yet summarizing performance with an evaluation metric is often insufficient: it assumes that the evaluation metr...scikit-learn.org/stable/inspection.html -
11. Dispatching — scikit-learn 1.6.1 documentation
Array API support (experimental)- Example usage, Support for Array API-compatible inputs, Common estimator checks..scikit-learn.org/stable/dispatching.html -
SVM with custom kernel — scikit-learn 1.6.1 doc...
Simple usage of Support Vector Machines to classify a sample. It will plot the decision surface and the support vectors. Total running time of the script:(0 minutes 0.103 seconds) Launch binder Lau...scikit-learn.org/stable/auto_examples/svm/plot_custom_kernel.html -
GMM Initialization Methods — scikit-learn 1.6.1...
Examples of the different methods of initialization in Gaussian Mixture Models See Gaussian mixture models for more information on the estimator. Here we generate some sample data with four easy to...scikit-learn.org/stable/auto_examples/mixture/plot_gmm_init.html -
Species distribution modeling — scikit-learn 1....
Modeling species’ geographic distributions is an important problem in conservation biology. In this example, we model the geographic distribution of two South American mammals given past observatio...scikit-learn.org/stable/auto_examples/applications/plot_species_distribution_modeling.html -
Probability calibration of classifiers — scikit...
When performing classification you often want to predict not only the class label, but also the associated probability. This probability gives you some kind of confidence on the prediction. However...scikit-learn.org/stable/auto_examples/calibration/plot_calibration.html -
Kernel Density Estimation — scikit-learn 1.6.1 ...
This example shows how kernel density estimation (KDE), a powerful non-parametric density estimation technique, can be used to learn a generative model for a dataset. With this generative model in ...scikit-learn.org/stable/auto_examples/neighbors/plot_digits_kde_sampling.html -
Missing Value Imputation — scikit-learn 1.6.1 d...
Examples concerning the sklearn.impute module. Imputing missing values before building an estimator Imputing missing values with variants of IterativeImputerscikit-learn.org/stable/auto_examples/impute/index.html -
permutation_importance — scikit-learn 1.6.1 doc...
Gallery examples: Release Highlights for scikit-learn 0.22 Feature importances with a forest of trees Gradient Boosting regression Permutation Importance vs Random Forest Feature Importance (MDI) P...scikit-learn.org/stable/modules/generated/sklearn.inspection.permutation_importance.html -
sklearn.preprocessing — scikit-learn 1.6.1 docu...
Methods for scaling, centering, normalization, binarization, and more. User guide. See the Preprocessing data section for further details.scikit-learn.org/stable/api/sklearn.preprocessing.html