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Results 611 - 620 of 3,496 for document (1.96 seconds)

  1. L1-based models for Sparse Signals — scik...

    The present example compares three l1-based regression models on a synthetic signal obtained from sparse and correlated features that are further corrupted with additive Gaussian noise: a Lasso;, a...
    scikit-learn.org/stable/auto_examples/linear_model/plot_lasso_and_elasticnet.html
    Mon Jan 19 11:28:25 GMT 2026
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  2. Ridge coefficients as a function of the L2 Regu...

    A model that overfits learns the training data too well, capturing both the underlying patterns and the noise in the data. However, when applied to unseen data, the learned associations may not hol...
    scikit-learn.org/stable/auto_examples/linear_model/plot_ridge_coeffs.html
    Mon Jan 19 11:28:25 GMT 2026
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  3. Gaussian processes on discrete data structures ...

    This example illustrates the use of Gaussian processes for regression and classification tasks on data that are not in fixed-length feature vector form. This is achieved through the use of kernel f...
    scikit-learn.org/stable/auto_examples/gaussian_process/plot_gpr_on_structured_data.html
    Mon Jan 19 11:28:24 GMT 2026
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  4. Curve Fitting with Bayesian Ridge Regression &#...

    Computes a Bayesian Ridge Regression of Sinusoids. See Bayesian Ridge Regression for more information on the regressor. In general, when fitting a curve with a polynomial by Bayesian ridge regressi...
    scikit-learn.org/stable/auto_examples/linear_model/plot_bayesian_ridge_curvefit.html
    Mon Jan 19 11:28:23 GMT 2026
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  5. Model-based and sequential feature selection &#...

    This example illustrates and compares two approaches for feature selection: SelectFromModel which is based on feature importance, and SequentialFeatureSelector which relies on a greedy approach. We...
    scikit-learn.org/stable/auto_examples/feature_selection/plot_select_from_model_diabetes.html
    Mon Jan 19 11:28:25 GMT 2026
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  6. Ordinary Least Squares and Ridge Regression &#8...

    Ordinary Least Squares: We illustrate how to use the ordinary least squares (OLS) model, LinearRegression, on a single feature of the diabetes dataset. We train on a subset of the data, evaluate on...
    scikit-learn.org/stable/auto_examples/linear_model/plot_ols_ridge.html
    Mon Jan 19 11:28:25 GMT 2026
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  7. Evaluate the performance of a classifier with C...

    Example of confusion matrix usage to evaluate the quality of the output of a classifier on the iris data set. The diagonal elements represent the number of points for which the predicted label is e...
    scikit-learn.org/stable/auto_examples/model_selection/plot_confusion_matrix.html
    Mon Jan 19 11:28:24 GMT 2026
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  8. Detection error tradeoff (DET) curve — sc...

    In this example, we compare two binary classification multi-threshold metrics: the Receiver Operating Characteristic (ROC) and the Detection Error Tradeoff (DET). For such purpose, we evaluate two ...
    scikit-learn.org/stable/auto_examples/model_selection/plot_det.html
    Mon Jan 19 11:28:23 GMT 2026
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  9. Effect of varying threshold for self-training &...

    This example illustrates the effect of a varying threshold on self-training. The breast_cancer dataset is loaded, and labels are deleted such that only 50 out of 569 samples have labels. A SelfTrai...
    scikit-learn.org/stable/auto_examples/semi_supervised/plot_self_training_varying_threshold.html
    Mon Jan 19 11:28:25 GMT 2026
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  10. Kernel Density Estimate of Species Distribution...

    This shows an example of a neighbors-based query (in particular a kernel density estimate) on geospatial data, using a Ball Tree built upon the Haversine distance metric – i.e. distances over point...
    scikit-learn.org/stable/auto_examples/neighbors/plot_species_kde.html
    Mon Jan 19 11:28:25 GMT 2026
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