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  1. fast_logdet — scikit-learn 1.5.2 documentation

    Skip to main content Back to top Ctrl + K GitHub fast_logdet # sklearn.utils.extmath. fast_logdet ( A ) [source] # Co...
    scikit-learn.org/stable/modules/generated/sklearn.utils.extmath.fast_logdet.html
    Thu Nov 21 22:17:08 UTC 2024
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  2. murmurhash3_32 — scikit-learn 1.5.2 documentation

    Skip to main content Back to top Ctrl + K GitHub murmurhash3_32 # sklearn.utils. murmurhash3_32 ( key , seed = 0 , po...
    scikit-learn.org/stable/modules/generated/sklearn.utils.murmurhash3_32.html
    Thu Nov 21 22:17:08 UTC 2024
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  3. cosine_distances — scikit-learn 1.5.2 documenta...

    Skip to main content Back to top Ctrl + K GitHub cosine_distances # sklearn.metrics.pairwise. cosine_distances ( X , ...
    scikit-learn.org/stable/modules/generated/sklearn.metrics.pairwise.cosine_distances.html
    Fri Nov 22 23:53:26 UTC 2024
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  4. Caching nearest neighbors — scikit-learn 1.5.2 ...

    This examples demonstrates how to precompute the k nearest neighbors before using them in KNeighborsClassifier. KNeighborsClassifier can compute the nearest neighbors internally, but precomputing t...
    scikit-learn.org/stable/auto_examples/neighbors/plot_caching_nearest_neighbors.html
    Fri Nov 22 23:53:27 UTC 2024
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  5. Comparison of Calibration of Classifiers — scik...

    Well calibrated classifiers are probabilistic classifiers for which the output of predict_proba can be directly interpreted as a confidence level. For instance, a well calibrated (binary) classifie...
    scikit-learn.org/stable/auto_examples/calibration/plot_compare_calibration.html
    Fri Nov 22 23:53:26 UTC 2024
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  6. Vector Quantization Example — scikit-learn 1.5....

    This example shows how one can use KBinsDiscretizer to perform vector quantization on a set of toy image, the raccoon face. Original image: We start by loading the raccoon face image from SciPy. We...
    scikit-learn.org/stable/auto_examples/cluster/plot_face_compress.html
    Fri Nov 22 23:53:26 UTC 2024
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  7. cross_validate — scikit-learn 1.5.2 documentation

    Gallery examples: Release Highlights for scikit-learn 1.4 Categorical Feature Support in Gradient Boosting Combine predictors using stacking Features in Histogram Gradient Boosting Trees Lagged fea...
    scikit-learn.org/stable/modules/generated/sklearn.model_selection.cross_validate.html
    Fri Nov 22 23:53:27 UTC 2024
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  8. Plotting Validation Curves — scikit-learn 1.5.2...

    In this plot you can see the training scores and validation scores of an SVM for different values of the kernel parameter gamma. For very low values of gamma, you can see that both the training sco...
    scikit-learn.org/stable/auto_examples/model_selection/plot_validation_curve.html
    Fri Nov 22 23:53:26 UTC 2024
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  9. recall_score — scikit-learn 1.5.2 documentation

    Gallery examples: Probability Calibration curves Post-tuning the decision threshold for cost-sensitive learning Precision-Recall
    scikit-learn.org/stable/modules/generated/sklearn.metrics.recall_score.html
    Fri Nov 22 23:53:26 UTC 2024
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  10. Orthogonal Matching Pursuit — scikit-learn 1.5....

    Using orthogonal matching pursuit for recovering a sparse signal from a noisy measurement encoded with a dictionary Total running time of the script:(0 minutes 0.203 seconds) Launch binder Launch J...
    scikit-learn.org/stable/auto_examples/linear_model/plot_omp.html
    Fri Nov 22 23:53:26 UTC 2024
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