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  1. 3.3. Tuning the decision threshold for class pr...

    Classification is best divided into two parts: the statistical problem of learning a model to predict, ideally, class probabilities;, the decision problem to take concrete action based on those pro...
    scikit-learn.org/stable/modules/classification_threshold.html
    Fri Nov 22 23:53:27 UTC 2024
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  2. Plot multinomial and One-vs-Rest Logistic Regre...

    Plot decision surface of multinomial and One-vs-Rest Logistic Regression. The hyperplanes corresponding to the three One-vs-Rest (OVR) classifiers are represented by the dashed lines.,., Total runn...
    scikit-learn.org/stable/auto_examples/linear_model/plot_logistic_multinomial.html
    Sat Nov 23 04:49:15 UTC 2024
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  3. t-SNE: The effect of various perplexity values ...

    An illustration of t-SNE on the two concentric circles and the S-curve datasets for different perplexity values. We observe a tendency towards clearer shapes as the perplexity value increases. The ...
    scikit-learn.org/stable/auto_examples/manifold/plot_t_sne_perplexity.html
    Sat Nov 23 04:49:15 UTC 2024
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  4. Illustration of Gaussian process classification...

    This example illustrates GPC on XOR data. Compared are a stationary, isotropic kernel (RBF) and a non-stationary kernel (DotProduct). On this particular dataset, the DotProduct kernel obtains consi...
    scikit-learn.org/stable/auto_examples/gaussian_process/plot_gpc_xor.html
    Sat Nov 23 04:49:16 UTC 2024
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  5. Model selection with Probabilistic PCA and Fact...

    Probabilistic PCA and Factor Analysis are probabilistic models. The consequence is that the likelihood of new data can be used for model selection and covariance estimation. Here we compare PCA and...
    scikit-learn.org/stable/auto_examples/decomposition/plot_pca_vs_fa_model_selection.html
    Sat Nov 23 04:49:15 UTC 2024
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  6. inplace_csr_row_normalize_l1 — scikit-learn 1.5...

    Skip to main content Back to top Ctrl + K GitHub inplace_csr_row_normalize_l1 # sklearn.utils.sparsefuncs_fast. inpla...
    scikit-learn.org/stable/modules/generated/sklearn.utils.sparsefuncs_fast.inplace_csr_row_normaliz...
    Sat Nov 23 04:49:16 UTC 2024
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  7. Elasticsearch — built on the Elastic Search AI ...

    optimized for GenAI View search documentation Search applications For...controls search access with its document-level security. Learn more...
    www.elastic.co/enterprise-search
    Sun Nov 24 00:04:23 UTC 2024
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  8. Normal, Ledoit-Wolf and OAS Linear Discriminant...

    This example illustrates how the Ledoit-Wolf and Oracle Approximating Shrinkage (OAS) estimators of covariance can improve classification. Total running time of the script:(0 minutes 8.053 seconds)...
    scikit-learn.org/stable/auto_examples/classification/plot_lda.html
    Sat Nov 23 04:49:14 UTC 2024
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  9. Bisecting K-Means and Regular K-Means Performan...

    This example shows differences between Regular K-Means algorithm and Bisecting K-Means. While K-Means clusterings are different when increasing n_clusters, Bisecting K-Means clustering builds on to...
    scikit-learn.org/stable/auto_examples/cluster/plot_bisect_kmeans.html
    Sat Nov 23 04:49:15 UTC 2024
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  10. Iso-probability lines for Gaussian Processes cl...

    A two-dimensional classification example showing iso-probability lines for the predicted probabilities., Total running time of the script:(0 minutes 0.146 seconds) Launch binder Launch JupyterLite ...
    scikit-learn.org/stable/auto_examples/gaussian_process/plot_gpc_isoprobability.html
    Sat Nov 23 04:49:16 UTC 2024
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