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mean_gamma_deviance — scikit-learn 1.7.1 docume...
Skip to main content Back to top Ctrl + K GitHub Choose version mean_gamma_deviance # sklearn.metrics. mean_gamma_dev...scikit-learn.org/stable/modules/generated/sklearn.metrics.mean_gamma_deviance.html -
fetch_california_housing — scikit-learn 1.7.1 d...
Gallery examples: Comparing Random Forests and Histogram Gradient Boosting models Early stopping in Gradient Boosting Imputing missing values with variants of IterativeImputer Imputing missing valu...scikit-learn.org/stable/modules/generated/sklearn.datasets.fetch_california_housing.html -
mean_absolute_error — scikit-learn 1.7.1 docume...
Gallery examples: Lagged features for time series forecasting Poisson regression and non-normal loss Quantile regression Tweedie regression on insurance claimsscikit-learn.org/stable/modules/generated/sklearn.metrics.mean_absolute_error.html -
multilabel_confusion_matrix — scikit-learn 1.7....
Skip to main content Back to top Ctrl + K GitHub Choose version multilabel_confusion_matrix # sklearn.metrics. multil...scikit-learn.org/stable/modules/generated/sklearn.metrics.multilabel_confusion_matrix.html -
mean_squared_error — scikit-learn 1.7.1 documen...
Gallery examples: Model Complexity Influence Early stopping in Gradient Boosting Prediction Intervals for Gradient Boosting Regression Gradient Boosting regression Ordinary Least Squares and Ridge ...scikit-learn.org/stable/modules/generated/sklearn.metrics.mean_squared_error.html -
sklearn.random_projection — scikit-learn 1.7.1 ...
Random projection transformers. Random projections are a simple and computationally efficient way to reduce the dimensionality of the data by trading a controlled amount of accuracy (as additional ...scikit-learn.org/stable/api/sklearn.random_projection.html -
sklearn.kernel_approximation — scikit-learn 1.7...
Approximate kernel feature maps based on Fourier transforms and count sketches. User guide. See the Kernel Approximation section for further details.scikit-learn.org/stable/api/sklearn.kernel_approximation.html -
fetch_species_distributions — scikit-learn 1.7....
Gallery examples: Species distribution modeling Kernel Density Estimate of Species Distributionsscikit-learn.org/stable/modules/generated/sklearn.datasets.fetch_species_distributions.html -
load_sample_image — scikit-learn 1.7.1 document...
Skip to main content Back to top Ctrl + K GitHub Choose version load_sample_image # sklearn.datasets. load_sample_ima...scikit-learn.org/stable/modules/generated/sklearn.datasets.load_sample_image.html -
sklearn.naive_bayes — scikit-learn 1.7.1 docume...
Naive Bayes algorithms. These are supervised learning methods based on applying Bayes’ theorem with strong (naive) feature independence assumptions. User guide. See the Naive Bayes section for furt...scikit-learn.org/stable/api/sklearn.naive_bayes.html