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get_scorer — scikit-learn 1.7.0 documentation
Gallery examples: Release Highlights for scikit-learn 1.4scikit-learn.org/stable/modules/generated/sklearn.metrics.get_scorer.html -
k_means — scikit-learn 1.7.0 documentation
Skip to main content Back to top Ctrl + K GitHub Choose version k_means # sklearn.cluster. k_means ( X , n_clusters ,...scikit-learn.org/stable/modules/generated/sklearn.cluster.k_means.html -
check_memory — scikit-learn 1.7.0 documentation
Skip to main content Back to top Ctrl + K GitHub Choose version check_memory # sklearn.utils.validation. check_memory...scikit-learn.org/stable/modules/generated/sklearn.utils.validation.check_memory.html -
sklearn.metrics — scikit-learn 1.7.0 documentation
Score functions, performance metrics, pairwise metrics and distance computations. User guide. See the Metrics and scoring: quantifying the quality of predictions and Pairwise metrics, Affinities an...scikit-learn.org/stable/api/sklearn.metrics.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 -
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 -
Univariate Feature Selection — scikit-learn 1.7...
This notebook is an example of using univariate feature selection to improve classification accuracy on a noisy dataset. In this example, some noisy (non informative) features are added to the iris...scikit-learn.org/stable/auto_examples/feature_selection/plot_feature_selection.html -
load_iris — scikit-learn 1.7.0 documentation
Gallery examples: Plot classification probability Plot Hierarchical Clustering Dendrogram Concatenating multiple feature extraction methods Incremental PCA Principal Component Analysis (PCA) on Iri...scikit-learn.org/stable/modules/generated/sklearn.datasets.load_iris.html -
SVM with custom kernel — scikit-learn 1.7.0 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 0.090 seconds) Launch binder Lau...scikit-learn.org/stable/auto_examples/svm/plot_custom_kernel.html -
Kernel Density Estimation — scikit-learn 1.7.0 ...
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