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Inductive Clustering — scikit-learn 1.8.0 docum...
Clustering can be expensive, especially when our dataset contains millions of datapoints. Many clustering algorithms are not inductive and so cannot be directly applied to new data samples without ...scikit-learn.org/stable/auto_examples/cluster/plot_inductive_clustering.html -
Release Highlights — scikit-learn 1.8.0 documen...
These examples illustrate the main features of the releases of scikit-learn. Release Highlights for scikit-learn 1.8 Release Highlights for scikit-learn 1.7 Release Highlights for scikit-learn 1.6 ...scikit-learn.org/stable/auto_examples/release_highlights/index.html -
Frozen Estimators — scikit-learn 1.8.0 document...
Examples concerning the sklearn.frozen module. Examples of Using FrozenEstimatorscikit-learn.org/stable/auto_examples/frozen/index.html -
Cross decomposition — scikit-learn 1.8.0 docume...
Examples concerning the sklearn.cross_decomposition module. Compare cross decomposition methods Principal Component Regression vs Partial Least Squares Regressionscikit-learn.org/stable/auto_examples/cross_decomposition/index.html -
Quantile regression — scikit-learn 1.8.0 docume...
This example illustrates how quantile regression can predict non-trivial conditional quantiles. The left figure shows the case when the error distribution is normal, but has non-constant variance, ...scikit-learn.org/stable/auto_examples/linear_model/plot_quantile_regression.html -
Nearest Neighbors — scikit-learn 1.8.0 document...
Examples concerning the sklearn.neighbors module. Approximate nearest neighbors in TSNE Caching nearest neighbors Comparing Nearest Neighbors with and without Neighborhood Components Analysis Dimen...scikit-learn.org/stable/auto_examples/neighbors/index.html -
sklearn.metrics — scikit-learn 1.8.0 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 -
5. Inspection — scikit-learn 1.8.0 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 -
sklearn.preprocessing — scikit-learn 1.8.0 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 -
12. Dispatching — scikit-learn 1.8.0 documentation
Array API support (experimental)- Enabling array API support, Example usage, Support for Array API-compatible inputs, Input and output array type handling, Common estimator checks..scikit-learn.org/stable/dispatching.html