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A demo of the Spectral Co-Clustering algorithm ...
consensus score: 1.000 # Authors: The scikit-learn...permutation ( data . shape [ 1 ]) data = data [ row_idx ][:,...scikit-learn.org/stable/auto_examples/bicluster/plot_spectral_coclustering.html -
L1 Penalty and Sparsity in Logistic Regression ...
axes_row ) in enumerate ( zip (( 1 , 0.1 , 0.01 ), axes )): # Increase...of the models for varying C. C=1.00 Sparsity with L1 penalty: 4.69%...scikit-learn.org/stable/auto_examples/linear_model/plot_logistic_l1_l2_sparsity.html -
1.11. Ensembles: Gradient boosting, random fore...
Gradient Boosting models 1.11.1.1.1. Usage # Most of the parameters...= [[ 1 , 0 ], ... [ 1 , 0 ], ... [ 1 , 0 ], ... [ 0 , 1 ]] >>>...scikit-learn.org/stable/modules/ensemble.html -
get_config — scikit-learn 1.6.1 documentation
Skip to main content Back to top Ctrl + K GitHub Choose version get_config # sklearn. get_config ( ) [source] # Retri...scikit-learn.org/stable/modules/generated/sklearn.get_config.html -
sklearn.svm — scikit-learn 1.6.1 documentation
Support vector machine algorithms. User guide. See the Support Vector Machines section for further details.scikit-learn.org/stable/api/sklearn.svm.html -
sklearn.tree — scikit-learn 1.6.1 documentation
Decision tree based models for classification and regression. User guide. See the Decision Trees section for further details. Exporting: Plotting:scikit-learn.org/stable/api/sklearn.tree.html -
sklearn.dummy — scikit-learn 1.6.1 documentation
Dummy estimators that implement simple rules of thumb. User guide. See the Metrics and scoring: quantifying the quality of predictions section for further details.scikit-learn.org/stable/api/sklearn.dummy.html -
sklearn.frozen — scikit-learn 1.6.1 documentation
Skip to main content Back to top Ctrl + K GitHub Choose version sklearn.frozen # FrozenEstimator Estimator that wraps...scikit-learn.org/stable/api/sklearn.frozen.html -
sklearn.utils — scikit-learn 1.6.1 documentation
Various utilities to help with development. Developer guide. See the Utilities for Developers section for further details. Input and parameter validation: Functions to validate input and parameters...scikit-learn.org/stable/api/sklearn.utils.html -
sklearn.multiclass — scikit-learn 1.6.1 documen...
Multiclass learning algorithms. one-vs-the-rest / one-vs-all, one-vs-one, error correcting output codes. The estimators provided in this module are meta-estimators: they require a base estimator to...scikit-learn.org/stable/api/sklearn.multiclass.html