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  1. 6. Visualizations — scikit-learn 1.7.2 do...

    Scikit-learn defines a simple API for creating visualizations for machine learning. The key feature of this API is to allow for quick plotting and visual adjustments without recalculation. We provi...
    scikit-learn.org/stable/visualizations.html
    2025-11-15 10:03
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  2. Regularization path of L1- Logistic Regression ...

    Train l1-penalized logistic regression models on a binary classification problem derived from the Iris dataset. The models are ordered from strongest regularized to least regularized. The 4 coeffic...
    scikit-learn.org/stable/auto_examples/linear_model/plot_logistic_path.html
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  3. Segmenting the picture of greek coins in region...

    This example uses Spectral clustering on a graph created from voxel-to-voxel difference on an image to break this image into multiple partly-homogeneous regions. This procedure (spectral clustering...
    scikit-learn.org/stable/auto_examples/cluster/plot_coin_segmentation.html
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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
    2025-11-15 10:03
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  5. Comparison of LDA and PCA 2D projection of Iris...

    The Iris dataset represents 3 kind of Iris flowers (Setosa, Versicolour and Virginica) with 4 attributes: sepal length, sepal width, petal length and petal width. Principal Component Analysis (PCA)...
    scikit-learn.org/stable/auto_examples/decomposition/plot_pca_vs_lda.html
    2025-11-15 10:03
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  6. A demo of the mean-shift clustering algorithm &...

    Reference: Dorin Comaniciu and Peter Meer, “Mean Shift: A robust approach toward feature space analysis”. IEEE Transactions on Pattern Analysis and Machine Intelligence. 2002. pp. 603-619. Generate...
    scikit-learn.org/stable/auto_examples/cluster/plot_mean_shift.html
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  7. Getting Started — scikit-learn 1.7.2 docu...

    Scikit-learn is an open source machine learning library that supports supervised and unsupervised learning. It also provides various tools for model fitting, data preprocessing, model selection, mo...
    scikit-learn.org/stable/getting_started.html
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  8. Cross decomposition — scikit-learn 1.7.2 ...

    Examples concerning the sklearn.cross_decomposition module. Compare cross decomposition methods Principal Component Regression vs Partial Least Squares Regression
    scikit-learn.org/stable/auto_examples/cross_decomposition/index.html
    2025-11-15 10:03
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  9. Release Highlights — scikit-learn 1.7.2 d...

    These examples illustrate the main features of the releases of scikit-learn. Release Highlights for scikit-learn 1.7 Release Highlights for scikit-learn 1.6 Release Highlights for scikit-learn 1.5 ...
    scikit-learn.org/stable/auto_examples/release_highlights/index.html
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  10. Nearest Neighbors — scikit-learn 1.7.2 do...

    Examples concerning the sklearn.neighbors module. Approximate nearest neighbors in TSNE Caching nearest neighbors Comparing Nearest Neighbors with and without Neighborhood Components Analysis Dimen...
    scikit-learn.org/stable/auto_examples/neighbors/index.html
    2025-11-15 10:03
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