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  1. Nested versus non-nested cross-validation — sci...

    This example compares non-nested and nested cross-validation strategies on a classifier of the iris data set. Nested cross-validation (CV) is often used to train a model in which hyperparameters al...
    scikit-learn.org/stable/auto_examples/model_selection/plot_nested_cross_validation_iris.html
    Thu Jul 03 11:42:06 UTC 2025
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  2. Principal Component Analysis (PCA) on Iris Data...

    This example shows a well known decomposition technique known as Principal Component Analysis (PCA) on the Iris dataset. This dataset is made of 4 features: sepal length, sepal width, petal length,...
    scikit-learn.org/stable/auto_examples/decomposition/plot_pca_iris.html
    Thu Jul 03 11:42:05 UTC 2025
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  3. Probabilistic predictions with Gaussian process...

    This example illustrates the predicted probability of GPC for an RBF kernel with different choices of the hyperparameters. The first figure shows the predicted probability of GPC with arbitrarily c...
    scikit-learn.org/stable/auto_examples/gaussian_process/plot_gpc.html
    Thu Jul 03 11:42:05 UTC 2025
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  4. Outlier detection with Local Outlier Factor (LO...

    The Local Outlier Factor (LOF) algorithm is an unsupervised anomaly detection method which computes the local density deviation of a given data point with respect to its neighbors. It considers as ...
    scikit-learn.org/stable/auto_examples/neighbors/plot_lof_outlier_detection.html
    Thu Jul 03 11:42:05 UTC 2025
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  5. Forecasting of CO2 level on Mona Loa dataset us...

    Documentation for GaussianProcessRegre...
    scikit-learn.org/stable/auto_examples/gaussian_process/plot_gpr_co2.html
    Thu Jul 03 11:42:05 UTC 2025
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  6. Visualizing cross-validation behavior in scikit...

    Choosing the right cross-validation object is a crucial part of fitting a model properly. There are many ways to split data into training and test sets in order to avoid model overfitting, to stand...
    scikit-learn.org/stable/auto_examples/model_selection/plot_cv_indices.html
    Thu Jul 03 11:42:05 UTC 2025
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  7. Comparing random forests and the multi-output m...

    An example to compare multi-output regression with random forest and the multioutput.MultiOutputRegressor meta-estimator. This example illustrates the use of the multioutput.MultiOutputRegressor me...
    scikit-learn.org/stable/auto_examples/ensemble/plot_random_forest_regression_multioutput.html
    Thu Jul 03 11:42:06 UTC 2025
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  8. 1.14. Semi-supervised learning — scikit-learn 1...

    Semi-supervised learning is a situation in which in your training data some of the samples are not labeled. The semi-supervised estimators in sklearn.semi_supervised are able to make use of this ad...
    scikit-learn.org/stable/modules/semi_supervised.html
    Thu Jul 03 11:42:05 UTC 2025
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  9. Comparing anomaly detection algorithms for outl...

    This example shows characteristics of different anomaly detection algorithms on 2D datasets. Datasets contain one or two modes (regions of high density) to illustrate the ability of algorithms to c...
    scikit-learn.org/stable/auto_examples/miscellaneous/plot_anomaly_comparison.html
    Thu Jul 03 11:42:05 UTC 2025
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  10. 1.2. Linear and Quadratic Discriminant Analysis...

    Linear Discriminant Analysis ( LinearDiscriminantAnalysis) and Quadratic Discriminant Analysis ( QuadraticDiscriminantAnalysis) are two classic classifiers, with, as their names suggest, a linear a...
    scikit-learn.org/stable/modules/lda_qda.html
    Thu Jul 03 11:42:06 UTC 2025
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