Search Options

Results per page
Sort
Preferred Languages
Labels
Advance

Results 1191 - 1200 of 1,703 for document (0.23 sec)

  1. 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
    Tue Jul 08 15:58:47 UTC 2025
      107.6K bytes
      Cache
     
  2. 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
    Tue Jul 08 15:58:49 UTC 2025
      96.3K bytes
      Cache
     
  3. 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
    Tue Jul 08 15:58:50 UTC 2025
      105.8K bytes
      Cache
     
  4. Hierarchical clustering: structured vs unstruct...

    Example builds a swiss roll dataset and runs hierarchical clustering on their position. For more information, see Hierarchical clustering. In a first step, the hierarchical clustering is performed ...
    scikit-learn.org/stable/auto_examples/cluster/plot_ward_structured_vs_unstructured.html
    Tue Jul 08 15:58:49 UTC 2025
      101.9K bytes
      Cache
     
  5. 7.4. Imputation of missing values — scikit-lear...

    For various reasons, many real world datasets contain missing values, often encoded as blanks, NaNs or other placeholders. Such datasets however are incompatible with scikit-learn estimators which ...
    scikit-learn.org/stable/modules/impute.html
    Tue Jul 08 15:58:50 UTC 2025
      84.4K bytes
      Cache
     
  6. 1.3. Kernel ridge regression — scikit-learn 1.7...

    Kernel ridge regression (KRR)[M2012] combines Ridge regression and classification(linear least squares with L_2-norm regularization) with the kernel trick. It thus learns a linear function in the s...
    scikit-learn.org/stable/modules/kernel_ridge.html
    Tue Jul 08 15:58:48 UTC 2025
      38.5K bytes
      1 views
      Cache
     
  7. d2_absolute_error_score — scikit-learn 1.7.0 do...

    Skip to main content Back to top Ctrl + K GitHub Choose version d2_absolute_error_score # sklearn.metrics. d2_absolut...
    scikit-learn.org/stable/modules/generated/sklearn.metrics.d2_absolute_error_score.html
    Tue Jul 08 15:58:48 UTC 2025
      113.1K bytes
      Cache
     
  8. adjusted_mutual_info_score — scikit-learn 1.7.0...

    Gallery examples: Adjustment for chance in clustering performance evaluation Demo of affinity propagation clustering algorithm Demo of DBSCAN clustering algorithm A demo of K-Means clustering on th...
    scikit-learn.org/stable/modules/generated/sklearn.metrics.adjusted_mutual_info_score.html
    Tue Jul 08 15:58:50 UTC 2025
      116.2K bytes
      Cache
     
  9. Get started with generative AI free | Elastic

    comprehensive documentation, plus support to help you...proprietary data (while using document-level protection to control...
    www.elastic.co/cloud/generative-ai-trial-overview
    Wed Jul 09 00:52:42 UTC 2025
      580.6K bytes
      1 views
      Cache
     
  10. incr_mean_variance_axis — scikit-learn 1.7.0 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 Jul 03 11:42:05 UTC 2025
      111.7K bytes
      Cache
     
Back to top