Search Options

Results per page
Sort
Preferred Languages
Labels
Advance

Results 771 - 780 of 1,682 for document (0.93 sec)

  1. Recursive feature elimination — scikit-learn 1....

    This example demonstrates how Recursive Feature Elimination ( RFE) can be used to determine the importance of individual pixels for classifying handwritten digits. RFE recursively removes the least...
    scikit-learn.org/stable/auto_examples/feature_selection/plot_rfe_digits.html
    Sat Apr 19 00:31:22 UTC 2025
      91.4K bytes
      Cache
     
  2. Gradient Boosting regression — scikit-learn 1.6...

    This example demonstrates Gradient Boosting to produce a predictive model from an ensemble of weak predictive models. Gradient boosting can be used for regression and classification problems. Here,...
    scikit-learn.org/stable/auto_examples/ensemble/plot_gradient_boosting_regression.html
    Sat Apr 19 00:31:22 UTC 2025
      109.9K bytes
      Cache
     
  3. Polynomial and Spline interpolation — scikit-le...

    This example demonstrates how to approximate a function with polynomials up to degree degree by using ridge regression. We show two different ways given n_samples of 1d points x_i: PolynomialFeatur...
    scikit-learn.org/stable/auto_examples/linear_model/plot_polynomial_interpolation.html
    Sat Apr 19 00:31:22 UTC 2025
      121.4K bytes
      Cache
     
  4. Generalized Linear Models — scikit-learn 1.6.1 ...

    Examples concerning the sklearn.linear_model module. Comparing Linear Bayesian Regressors Comparing various online solvers Curve Fitting with Bayesian Ridge Regression Decision Boundaries of Multin...
    scikit-learn.org/stable/auto_examples/linear_model/index.html
    Sat Apr 19 00:31:22 UTC 2025
      94.9K bytes
      Cache
     
  5. safe_mask — scikit-learn 1.6.1 documentation

    Skip to main content Back to top Ctrl + K GitHub Choose version safe_mask # sklearn.utils. safe_mask ( X , mask ) [so...
    scikit-learn.org/stable/modules/generated/sklearn.utils.safe_mask.html
    Sat Apr 19 00:31:22 UTC 2025
      106.9K bytes
      Cache
     
  6. all_estimators — scikit-learn 1.6.1 documentation

    Skip to main content Back to top Ctrl + K GitHub Choose version all_estimators # sklearn.utils.discovery. all_estimat...
    scikit-learn.org/stable/modules/generated/sklearn.utils.discovery.all_estimators.html
    Sat Apr 19 00:31:21 UTC 2025
      109.5K bytes
      Cache
     
  7. sklearn.pipeline — scikit-learn 1.6.1 documenta...

    Utilities to build a composite estimator as a chain of transforms and estimators. User guide. See the Pipelines and composite estimators section for further details.
    scikit-learn.org/stable/api/sklearn.pipeline.html
    Sat Apr 19 00:31:22 UTC 2025
      116.3K bytes
      Cache
     
  8. label_binarize — scikit-learn 1.6.1 documentation

    Gallery examples: Precision-Recall
    scikit-learn.org/stable/modules/generated/sklearn.preprocessing.label_binarize.html
    Sat Apr 19 00:31:22 UTC 2025
      110.7K bytes
      Cache
     
  9. check_cv — scikit-learn 1.6.1 documentation

    Skip to main content Back to top Ctrl + K GitHub Choose version check_cv # sklearn.model_selection. check_cv ( cv = 5...
    scikit-learn.org/stable/modules/generated/sklearn.model_selection.check_cv.html
    Sat Apr 19 00:31:22 UTC 2025
      109K bytes
      Cache
     
  10. r2_score — scikit-learn 1.6.1 documentation

    Gallery examples: L1-based models for Sparse Signals Non-negative least squares Ordinary Least Squares Example Failure of Machine Learning to infer causal effects Effect of transforming the targets...
    scikit-learn.org/stable/modules/generated/sklearn.metrics.r2_score.html
    Sat Apr 19 00:31:22 UTC 2025
      121.8K bytes
      Cache
     
Back to top