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  1. compute_class_weight — scikit-learn 1.7.2 docum...

    Skip to main content Back to top Ctrl + K GitHub Choose version compute_class_weight # sklearn.utils.class_weight. co...
    scikit-learn.org/stable/modules/generated/sklearn.utils.class_weight.compute_class_weight.html
    Tue Sep 23 15:14:21 UTC 2025
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  2. Post pruning decision trees with cost complexit...

    subplots ( 2 , 1 ) ax [ 0 ] . plot ( ccp_alphas...
    scikit-learn.org/stable/auto_examples/tree/plot_cost_complexity_pruning.html
    Tue Sep 23 15:14:23 UTC 2025
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  3. IsolationForest example — scikit-learn 1.7.2 do...

    2 ) @ covariance + np . array ([ 2 , 2 ]) # general...general cluster_2 = 0.3 * rng . randn ( n_samples , 2 ) + np . array...
    scikit-learn.org/stable/auto_examples/ensemble/plot_isolation_forest.html
    Tue Sep 23 15:14:21 UTC 2025
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  4. pair_confusion_matrix — scikit-learn 1.7.2 docu...

    2 ], [ 0 , 0 , 1 , 1 ]) array([[8, 2], [0, 2]]... Note...confusion matrix \(C\) computes a 2 by 2 similarity matrix between two...
    scikit-learn.org/stable/modules/generated/sklearn.metrics.cluster.pair_confusion_matrix.html
    Tue Sep 23 15:14:21 UTC 2025
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  5. Plot Hierarchical Clustering Dendrogram — sciki...

    This example plots the corresponding dendrogram of a hierarchical clustering using AgglomerativeClustering and the dendrogram method available in scipy. Total running time of the script:(0 minutes ...
    scikit-learn.org/stable/auto_examples/cluster/plot_agglomerative_dendrogram.html
    Tue Sep 23 15:14:23 UTC 2025
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  6. Demo of DBSCAN clustering algorithm — scikit-le...

    DBSCAN (Density-Based Spatial Clustering of Applications with Noise) finds core samples in regions of high density and expands clusters from them. This algorithm is good for data which contains clu...
    scikit-learn.org/stable/auto_examples/cluster/plot_dbscan.html
    Tue Sep 23 15:14:23 UTC 2025
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  7. OOB Errors for Random Forests — scikit-learn 1....

    The RandomForestClassifier is trained using bootstrap aggregation, where each new tree is fit from a bootstrap sample of the training observations z_i = (x_i, y_i). The out-of-bag(OOB) error is the...
    scikit-learn.org/stable/auto_examples/ensemble/plot_ensemble_oob.html
    Tue Sep 23 15:14:21 UTC 2025
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  8. Comparing Linear Bayesian Regressors — scikit-l...

    This example compares two different bayesian regressors: a Automatic Relevance Determination - ARD, a Bayesian Ridge Regression. In the first part, we use an Ordinary Least Squares(OLS) model as a ...
    scikit-learn.org/stable/auto_examples/linear_model/plot_ard.html
    Tue Sep 23 15:14:21 UTC 2025
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  9. inplace_swap_row — scikit-learn 1.7.2 documenta...

    2 , 3 , 3 , 3 ]) >>> indices = np . array ([ 0 , 2 , 2 ])...>>> data = np . array ([ 8 , 2 , 5 ]) >>> csr = sparse . csr_matrix...
    scikit-learn.org/stable/modules/generated/sklearn.utils.sparsefuncs.inplace_swap_row.html
    Mon Sep 22 13:26:32 UTC 2025
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  10. non_negative_factorization — scikit-learn 1.7.2...

    ||H||_{Fro}^2,\end{aligned}\end{align} \] where \(||A||_{Fro}^2 = \sum_{i,j}...array ([[ 1 , 1 ], [ 2 , 1 ], [ 3 , 1.2 ], [ 4 , 1 ], [ 5 , 0.8...
    scikit-learn.org/stable/modules/generated/sklearn.decomposition.non_negative_factorization.html
    Tue Sep 23 15:14:23 UTC 2025
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