Search Options

Results per page
Sort
Preferred Languages
Labels
Advance

Results 861 - 870 of 1,826 for document (0.21 sec)

  1. fast_logdet — scikit-learn 1.5.2 documentation

    Skip to main content Back to top Ctrl + K GitHub fast_logdet # sklearn.utils.extmath. fast_logdet ( A ) [source] # Co...
    scikit-learn.org/stable/modules/generated/sklearn.utils.extmath.fast_logdet.html
    Sat Nov 23 04:49:14 UTC 2024
      105.4K bytes
      Cache
     
  2. safe_sqr — scikit-learn 1.5.2 documentation

    Skip to main content Back to top Ctrl + K GitHub safe_sqr # sklearn.utils. safe_sqr ( X , * , copy = True ) [source] ...
    scikit-learn.org/stable/modules/generated/sklearn.utils.safe_sqr.html
    Sat Nov 23 04:49:15 UTC 2024
      104.5K bytes
      Cache
     
  3. parallel_backend — scikit-learn 1.5.2 documenta...

    Skip to main content Back to top Ctrl + K GitHub parallel_backend # class sklearn.utils. parallel_backend ( * args , ...
    scikit-learn.org/stable/modules/generated/sklearn.utils.parallel_backend.html
    Sat Nov 23 04:49:15 UTC 2024
      103.3K bytes
      Cache
     
  4. check_estimator — scikit-learn 1.5.2 documentation

    Skip to main content Back to top Ctrl + K GitHub check_estimator # sklearn.utils.estimator_checks. check_estimator ( ...
    scikit-learn.org/stable/modules/generated/sklearn.utils.estimator_checks.check_estimator.html
    Sat Nov 23 04:49:14 UTC 2024
      107.9K bytes
      Cache
     
  5. 2.2. Manifold learning — scikit-learn 1.5.2 doc...

    Look for the bare necessities, The simple bare necessities, Forget about your worries and your strife, I mean the bare necessities, Old Mother Nature’s recipes, That bring the bare necessities of l...
    scikit-learn.org/stable/modules/manifold.html
    Fri Nov 22 23:53:26 UTC 2024
      85.7K bytes
      Cache
     
  6. Robust linear model estimation using RANSAC — s...

    In this example, we see how to robustly fit a linear model to faulty data using the RANSAC algorithm. The ordinary linear regressor is sensitive to outliers, and the fitted line can easily be skewe...
    scikit-learn.org/stable/auto_examples/linear_model/plot_ransac.html
    Sat Nov 23 04:49:15 UTC 2024
      89.3K bytes
      Cache
     
  7. Compare the effect of different scalers on data...

    Feature 0 (median income in a block) and feature 5 (average house occupancy) of the California Housing dataset have very different scales and contain some very large outliers. These two characteris...
    scikit-learn.org/stable/auto_examples/preprocessing/plot_all_scaling.html
    Sat Nov 23 04:49:14 UTC 2024
      133.6K bytes
      Cache
     
  8. Label Propagation digits: Demonstrating perform...

    This example demonstrates the power of semisupervised learning by training a Label Spreading model to classify handwritten digits with sets of very few labels. The handwritten digit dataset has 179...
    scikit-learn.org/stable/auto_examples/semi_supervised/plot_label_propagation_digits.html
    Sat Nov 23 04:49:16 UTC 2024
      95.2K bytes
      Cache
     
  9. SVM: Maximum margin separating hyperplane — sci...

    Plot the maximum margin separating hyperplane within a two-class separable dataset using a Support Vector Machine classifier with linear kernel. Total running time of the script:(0 minutes 0.066 se...
    scikit-learn.org/stable/auto_examples/svm/plot_separating_hyperplane.html
    Sat Nov 23 04:49:15 UTC 2024
      83.6K bytes
      Cache
     
  10. Comparison between grid search and successive h...

    This example compares the parameter search performed by HalvingGridSearchCV and GridSearchCV. We first define the parameter space for an SVC estimator, and compute the time required to train a Halv...
    scikit-learn.org/stable/auto_examples/model_selection/plot_successive_halving_heatmap.html
    Sat Nov 23 04:49:16 UTC 2024
      101.6K bytes
      Cache
     
Back to top