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  1. Decision Boundaries of Multinomial and One-vs-R...

    This example compares decision boundaries of multinomial and one-vs-rest logistic regression on a 2D dataset with three classes. We make a comparison of the decision boundaries of both methods that...
    scikit-learn.org/stable/auto_examples/linear_model/plot_logistic_multinomial.html
    Tue Jul 08 15:58:49 UTC 2025
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  2. Decision boundary of semi-supervised classifier...

    A comparison for the decision boundaries generated on the iris dataset by Label Spreading, Self-training and SVM. This example demonstrates that Label Spreading and Self-training can learn good bou...
    scikit-learn.org/stable/auto_examples/semi_supervised/plot_semi_supervised_versus_svm_iris.html
    Tue Jul 08 15:58:50 UTC 2025
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  3. 2.9. Neural network models (unsupervised) — sci...

    Restricted Boltzmann machines: Restricted Boltzmann machines (RBM) are unsupervised nonlinear feature learners based on a probabilistic model. The features extracted by an RBM or a hierarchy of RBM...
    scikit-learn.org/stable/modules/neural_networks_unsupervised.html
    Tue Jul 08 15:58:50 UTC 2025
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  4. 5.1. Partial Dependence and Individual Conditio...

    Partial dependence plots (PDP) and individual conditional expectation (ICE) plots can be used to visualize and analyze interaction between the target response 1 and a set of input features of inter...
    scikit-learn.org/stable/modules/partial_dependence.html
    Tue Jul 08 15:58:50 UTC 2025
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  5. Retrieval Augmented Generation (RAG) - Elastics...

    g documents, webpages) that contain relevant...of source knowledge (e.g documents, webpages) that contain relevant...
    www.elastic.co/search-labs/blog/retrieval-augmented-generation-rag
    Wed Jul 09 01:16:57 UTC 2025
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  6. Elasticsearch Integrations | Elastic

    ingest your data check out the Document APIs as well as the Ingest...
    www.elastic.co/integrations
    Wed Jul 09 00:03:31 UTC 2025
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  7. 3.1. Cross-validation: evaluating estimator per...

    Learning the parameters of a prediction function and testing it on the same data is a methodological mistake: a model that would just repeat the labels of the samples that it has just seen would ha...
    scikit-learn.org/stable/modules/cross_validation.html
    Tue Jul 08 15:58:47 UTC 2025
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  8. Lasso model selection: AIC-BIC / cross-validati...

    This example focuses on model selection for Lasso models that are linear models with an L1 penalty for regression problems. Indeed, several strategies can be used to select the value of the regular...
    scikit-learn.org/stable/auto_examples/linear_model/plot_lasso_model_selection.html
    Tue Jul 08 15:58:47 UTC 2025
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  9. 3.5. Validation curves: plotting scores to eval...

    Every estimator has its advantages and drawbacks. Its generalization error can be decomposed in terms of bias, variance and noise. The bias of an estimator is its average error for different traini...
    scikit-learn.org/stable/modules/learning_curve.html
    Tue Jul 08 15:58:50 UTC 2025
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  10. Demonstration of multi-metric evaluation on cro...

    Multiple metric parameter search can be done by setting the scoring parameter to a list of metric scorer names or a dict mapping the scorer names to the scorer callables. The scores of all the scor...
    scikit-learn.org/stable/auto_examples/model_selection/plot_multi_metric_evaluation.html
    Tue Jul 08 15:58:49 UTC 2025
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