Search Options

Results per page
Sort
Preferred Languages
Labels
Advance

Results 1001 - 1010 of 1,742 for document (2.39 sec)

  1. The Johnson-Lindenstrauss bound for embedding w...

    on the 20 newsgroups text document (TF-IDF word frequencies)...newsgroups dataset some 300 documents with 100k features in total...
    scikit-learn.org/stable/auto_examples/miscellaneous/plot_johnson_lindenstrauss_bound.html
    Fri Oct 10 15:14:33 UTC 2025
      119.8K bytes
      Cache
     
  2. 1.17. Neural network models (supervised) — scik...

    details can be found in the documentation of SGD Adam is similar to...
    scikit-learn.org/stable/modules/neural_networks_supervised.html
    Fri Oct 10 15:14:33 UTC 2025
      66.8K bytes
      Cache
     
  3. incr_mean_variance_axis — scikit-learn 1.7.2 do...

    Skip to main content Back to top Ctrl + K GitHub Choose version incr_mean_variance_axis # sklearn.utils.sparsefuncs. ...
    scikit-learn.org/stable/modules/generated/sklearn.utils.sparsefuncs.incr_mean_variance_axis.html
    Thu Oct 09 16:57:45 UTC 2025
      111.7K bytes
      Cache
     
  4. 2.7. Novelty and Outlier Detection — scikit-lea...

    Many applications require being able to decide whether a new observation belongs to the same distribution as existing observations (it is an inlier), or should be considered as different (it is an ...
    scikit-learn.org/stable/modules/outlier_detection.html
    Fri Oct 10 15:14:33 UTC 2025
      72.6K bytes
      Cache
     
  5. reconstruct_from_patches_2d — scikit-learn 1.7....

    Gallery examples: Image denoising using dictionary learning
    scikit-learn.org/stable/modules/generated/sklearn.feature_extraction.image.reconstruct_from_patch...
    Fri Oct 10 15:14:35 UTC 2025
      110K bytes
      Cache
     
  6. A demo of structured Ward hierarchical clusteri...

    Compute the segmentation of a 2D image with Ward hierarchical clustering. The clustering is spatially constrained in order for each segmented region to be in one piece. Generate data: Resize it to ...
    scikit-learn.org/stable/auto_examples/cluster/plot_coin_ward_segmentation.html
    Fri Oct 10 15:14:35 UTC 2025
      94.3K bytes
      Cache
     
  7. Hashing feature transformation using Totally Ra...

    RandomTreesEmbedding provides a way to map data to a very high-dimensional, sparse representation, which might be beneficial for classification. The mapping is completely unsupervised and very effi...
    scikit-learn.org/stable/auto_examples/ensemble/plot_random_forest_embedding.html
    Fri Oct 10 15:14:35 UTC 2025
      102.5K bytes
      Cache
     
  8. normalized_mutual_info_score — scikit-learn 1.7...

    Gallery examples: Adjustment for chance in clustering performance evaluation
    scikit-learn.org/stable/modules/generated/sklearn.metrics.normalized_mutual_info_score.html
    Fri Oct 10 15:14:33 UTC 2025
      112.4K bytes
      Cache
     
  9. precision_recall_fscore_support — scikit-learn ...

    Skip to main content Back to top Ctrl + K GitHub Choose version precision_recall_fscore_support # sklearn.metrics. pr...
    scikit-learn.org/stable/modules/generated/sklearn.metrics.precision_recall_fscore_support.html
    Fri Oct 10 15:14:36 UTC 2025
      122.4K bytes
      Cache
     
  10. Comparing randomized search and grid search for...

    Compare randomized search and grid search for optimizing hyperparameters of a linear SVM with SGD training. All parameters that influence the learning are searched simultaneously (except for the nu...
    scikit-learn.org/stable/auto_examples/model_selection/plot_randomized_search.html
    Fri Oct 10 15:14:35 UTC 2025
      97.2K bytes
      Cache
     
Back to top