Search Options

Results per page
Sort
Preferred Languages
Labels
Advance

Results 821 - 830 of 1,549 for document (1.45 sec)

  1. L1-based models for Sparse Signals — scikit-lea...

    The present example compares three l1-based regression models on a synthetic signal obtained from sparse and correlated features that are further corrupted with additive gaussian noise: a Lasso;, a...
    scikit-learn.org/stable/auto_examples/linear_model/plot_lasso_and_elasticnet.html
    Sat Aug 23 16:32:04 UTC 2025
      125.4K bytes
      Cache
     
  2. 8.1. Toy datasets — scikit-learn 1.7.1 document...

    scikit-learn comes with a few small standard datasets that do not require to download any file from some external website. They can be loaded using the following functions: These datasets are usefu...
    scikit-learn.org/stable/datasets/toy_dataset.html
    Sat Aug 23 16:32:03 UTC 2025
      63.3K bytes
      1 views
      Cache
     
  3. 2.8. Density Estimation — scikit-learn 1.7.1 do...

    Density estimation walks the line between unsupervised learning, feature engineering, and data modeling. Some of the most popular and useful density estimation techniques are mixture models such as...
    scikit-learn.org/stable/modules/density.html
    Sat Aug 23 16:32:04 UTC 2025
      45.5K bytes
      Cache
     
  4. 9. Computing with scikit-learn — scikit-learn 1...

    Strategies to scale computationally: bigger data- Scaling with instances using out-of-core learning., Computational Performance- Prediction Latency, Prediction Throughput, Tips and Tricks., Paralle...
    scikit-learn.org/stable/computing.html
    Sat Aug 23 16:32:04 UTC 2025
      31.4K bytes
      Cache
     
  5. 1.10. Decision Trees — scikit-learn 1.7.1 docum...

    Decision Trees (DTs) are a non-parametric supervised learning method used for classification and regression. The goal is to create a model that predicts the value of a target variable by learning s...
    scikit-learn.org/stable/modules/tree.html
    Sat Aug 23 16:32:04 UTC 2025
      94.5K bytes
      Cache
     
  6. 1.16. Probability calibration — scikit-learn 1....

    When performing classification you often want not only to predict the class label, but also obtain a probability of the respective label. This probability gives you some kind of confidence on the p...
    scikit-learn.org/stable/modules/calibration.html
    Sat Aug 23 16:32:03 UTC 2025
      63.3K bytes
      Cache
     
  7. 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
    Sat Aug 23 16:32:03 UTC 2025
      66.7K bytes
      Cache
     
  8. safe_sqr — scikit-learn 1.7.1 documentation

    Skip to main content Back to top Ctrl + K GitHub Choose version safe_sqr # sklearn.utils. safe_sqr ( X , * , copy = T...
    scikit-learn.org/stable/modules/generated/sklearn.utils.safe_sqr.html
    Wed Aug 20 16:02:08 UTC 2025
      105.4K bytes
      Cache
     
  9. get_tags — scikit-learn 1.7.1 documentation

    Skip to main content Back to top Ctrl + K GitHub Choose version get_tags # sklearn.utils. get_tags ( estimator ) → Ta...
    scikit-learn.org/stable/modules/generated/sklearn.utils.get_tags.html
    Wed Aug 20 16:02:09 UTC 2025
      105.7K bytes
      Cache
     
  10. murmurhash3_32 — scikit-learn 1.7.1 documentation

    Skip to main content Back to top Ctrl + K GitHub Choose version murmurhash3_32 # sklearn.utils. murmurhash3_32 ( key ...
    scikit-learn.org/stable/modules/generated/sklearn.utils.murmurhash3_32.html
    Fri Aug 22 18:00:33 UTC 2025
      105.7K bytes
      Cache
     
Back to top