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Getting Started — scikit-learn 1.8.0 docu...
Scikit-learn is an open source machine learning library that supports supervised and unsupervised learning. It also provides various tools for model fitting, data preprocessing, model selection, mo...scikit-learn.org/stable/getting_started.html -
Frozen Estimators — scikit-learn 1.8.0 do...
Examples concerning the sklearn.frozen module. Examples of Using FrozenEstimatorscikit-learn.org/stable/auto_examples/frozen/index.html -
Quantile regression — scikit-learn 1.8.0 ...
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 do...
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.neural_network — scikit-learn 1.8...
Models based on neural networks. User guide. See the Neural network models (supervised) and Neural network models (unsupervised) sections for further details.scikit-learn.org/stable/api/sklearn.neural_network.html -
sklearn.preprocessing — scikit-learn 1.8....
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 -
sklearn.model_selection — scikit-learn 1....
Tools for model selection, such as cross validation and hyper-parameter tuning. User guide. See the Cross-validation: evaluating estimator performance, Tuning the hyper-parameters of an estimator, ...scikit-learn.org/stable/api/sklearn.model_selection.html -
sklearn.feature_selection — scikit-learn ...
Feature selection algorithms. These include univariate filter selection methods and the recursive feature elimination algorithm. User guide. See the Feature selection section for further details.scikit-learn.org/stable/api/sklearn.feature_selection.html -
sklearn.metrics — scikit-learn 1.8.0 docu...
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 -
sklearn.exceptions — scikit-learn 1.8.0 d...
Custom warnings and errors used across scikit-learn.scikit-learn.org/stable/api/sklearn.exceptions.html