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  1. Multiclass Receiver Operating Characteristic (R...

    This example describes the use of the Receiver Operating Characteristic (ROC) metric to evaluate the quality of multiclass classifiers. ROC curves typically feature true positive rate (TPR) on the ...
    scikit-learn.org/stable/auto_examples/model_selection/plot_roc.html
    Sat Nov 23 04:49:16 UTC 2024
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  2. Balance model complexity and cross-validated sc...

    This example balances model complexity and cross-validated score by finding a decent accuracy within 1 standard deviation of the best accuracy score while minimising the number of PCA components [1...
    scikit-learn.org/stable/auto_examples/model_selection/plot_grid_search_refit_callable.html
    Sat Nov 23 04:49:14 UTC 2024
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  3. Target Encoder’s Internal Cross fitting — sciki...

    The TargetEncoder replaces each category of a categorical feature with the shrunk mean of the target variable for that category. This method is useful in cases where there is a strong relationship ...
    scikit-learn.org/stable/auto_examples/preprocessing/plot_target_encoder_cross_val.html
    Sat Nov 23 04:49:16 UTC 2024
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  4. mean_absolute_percentage_error — scikit-learn 1...

    Gallery examples: Lagged features for time series forecasting
    scikit-learn.org/stable/modules/generated/sklearn.metrics.mean_absolute_percentage_error.html
    Sat Nov 23 04:49:15 UTC 2024
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  5. d2_log_loss_score — scikit-learn 1.5.2 document...

    Skip to main content Back to top Ctrl + K GitHub d2_log_loss_score # sklearn.metrics. d2_log_loss_score ( y_true , y_...
    scikit-learn.org/stable/modules/generated/sklearn.metrics.d2_log_loss_score.html
    Sat Nov 23 04:49:15 UTC 2024
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  6. 2.7. Novelty and Outlier Detection — scikit-lea...

    Many applications require being able to decide whether a new observation belongs to the same distribution as existing observations (it is an inlier), or should be considered as different (it is an ...
    scikit-learn.org/stable/modules/outlier_detection.html
    Fri Nov 22 23:53:26 UTC 2024
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  7. Hashing feature transformation using Totally Ra...

    RandomTreesEmbedding provides a way to map data to a very high-dimensional, sparse representation, which might be beneficial for classification. The mapping is completely unsupervised and very effi...
    scikit-learn.org/stable/auto_examples/ensemble/plot_random_forest_embedding.html
    Sat Nov 23 04:49:16 UTC 2024
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  8. A demo of structured Ward hierarchical clusteri...

    Compute the segmentation of a 2D image with Ward hierarchical clustering. The clustering is spatially constrained in order for each segmented region to be in one piece. Generate data: Resize it to ...
    scikit-learn.org/stable/auto_examples/cluster/plot_coin_ward_segmentation.html
    Sat Nov 23 04:49:16 UTC 2024
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  9. Plot multi-class SGD on the iris dataset — scik...

    Plot decision surface of multi-class SGD on iris dataset. The hyperplanes corresponding to the three one-versus-all (OVA) classifiers are represented by the dashed lines. Total running time of the ...
    scikit-learn.org/stable/auto_examples/linear_model/plot_sgd_iris.html
    Sat Nov 23 04:49:16 UTC 2024
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  10. Comparing randomized search and grid search for...

    Compare randomized search and grid search for optimizing hyperparameters of a linear SVM with SGD training. All parameters that influence the learning are searched simultaneously (except for the nu...
    scikit-learn.org/stable/auto_examples/model_selection/plot_randomized_search.html
    Sat Nov 23 04:49:16 UTC 2024
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