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  1. 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 Apr 21 17:07:39 UTC 2025
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  2. Ledoit-Wolf vs OAS estimation — scikit-learn 1....

    The usual covariance maximum likelihood estimate can be regularized using shrinkage. Ledoit and Wolf proposed a close formula to compute the asymptotically optimal shrinkage parameter (minimizing a...
    scikit-learn.org/stable/auto_examples/covariance/plot_lw_vs_oas.html
    Mon Apr 21 17:07:39 UTC 2025
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  3. 8. Computing with scikit-learn — scikit-learn 1...

    Strategies to scale computationally: bigger data- Scaling with instances using out-of-core learning., Computational Performance- Prediction Latency, Prediction Throughput, Tips and Tricks., Paralle...
    scikit-learn.org/stable/computing.html
    Mon Apr 21 17:07:39 UTC 2025
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  4. 2.8. Density Estimation — scikit-learn 1.6.1 do...

    Density estimation walks the line between unsupervised learning, feature engineering, and data modeling. Some of the most popular and useful density estimation techniques are mixture models such as...
    scikit-learn.org/stable/modules/density.html
    Mon Apr 21 17:07:39 UTC 2025
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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 Apr 21 17:07:39 UTC 2025
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  6. L1-based models for Sparse Signals — scikit-lea...

    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 Apr 21 17:07:39 UTC 2025
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  7. Plot different SVM classifiers in the iris data...

    Comparison of different linear SVM classifiers on a 2D projection of the iris dataset. We only consider the first 2 features of this dataset: Sepal length, Sepal width. This example shows how to pl...
    scikit-learn.org/stable/auto_examples/svm/plot_iris_svc.html
    Mon Apr 21 17:07:39 UTC 2025
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  8. Comparison of the K-Means and MiniBatchKMeans c...

    We want to compare the performance of the MiniBatchKMeans and KMeans: the MiniBatchKMeans is faster, but gives slightly different results (see Mini Batch K-Means). We will cluster a set of data, fi...
    scikit-learn.org/stable/auto_examples/cluster/plot_mini_batch_kmeans.html
    Mon Apr 21 17:07:38 UTC 2025
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  9. MNIST classification using multinomial logistic...

    Here we fit a multinomial logistic regression with L1 penalty on a subset of the MNIST digits classification task. We use the SAGA algorithm for this purpose: this a solver that is fast when the nu...
    scikit-learn.org/stable/auto_examples/linear_model/plot_sparse_logistic_regression_mnist.html
    Mon Apr 21 17:07:39 UTC 2025
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  10. Gaussian Processes regression: basic introducto...

    A simple one-dimensional regression example computed in two different ways: A noise-free case, A noisy case with known noise-level per datapoint. In both cases, the kernel’s parameters are estimate...
    scikit-learn.org/stable/auto_examples/gaussian_process/plot_gpr_noisy_targets.html
    Mon Apr 21 17:07:39 UTC 2025
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