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Comparing Target Encoder with Other Encoders — ...
pipe ): result = cross_validate ( pipe , X , y , scoring...categorical_features ), ] ) pipe = make_pipeline ( preprocessor...scikit-learn.org/stable/auto_examples/preprocessing/plot_target_encoder.html -
7.1. Pipelines and composite estimators — sciki...
SVC ())] >>> pipe = Pipeline ( estimators ) >>> pipe Pipeline(s...pipeline: >>> pipe . steps [ 0 ] ('reduce_dim', PCA()) >>> pipe [ 0 ]...scikit-learn.org/stable/modules/compose.html -
Displaying Pipelines — scikit-learn 1.7.0 docum...
] pipe = Pipeline ( steps ) pipe # click on the...= "linear" ))] pipe = Pipeline ( steps ) pipe # click on the...scikit-learn.org/stable/auto_examples/miscellaneous/plot_pipeline_display.html -
auto_examples_python.zip
pipe.set_params(max_depth=3, max_iter=15) else: pipe.set_params(... step=1)), ] ) pipe.fit(X, y) ranking = pipe.named_steps["rf...scikit-learn.org/stable/_downloads/07fcc19ba03226cd3d83d4e40ec44385/auto_examples_python.zip -
Release Highlights for scikit-learn 1.0 — sciki...
LogisticRegression ()) pipe . fit ( X , y ) pipe [: - 1 ] . get_feature_names_out...the latest version (with pip): pip install -- upgrade scikit...scikit-learn.org/stable/auto_examples/release_highlights/plot_release_highlights_1_0_0.html -
"It's over." | MetaFilter
(and I don't hit the hopium pipe anymore), I am in agreement...www.metafilter.com/209457/Its-over -
Recursive feature elimination — scikit-learn 1....
] ) pipe . fit ( X , y ) ranking = pipe . named_steps...), - 1 )) y = digits . target pipe = Pipeline ( [ ( "scaler" ,...scikit-learn.org/stable/auto_examples/feature_selection/plot_rfe_digits.html -
preprocessing.rst.txt
random_state=42) >>> pipe = make_pipeline(StandardScaler(),..., LogisticRegression()) >>> pipe.fit(X_train, y_train) # apply...scikit-learn.org/stable/_sources/modules/preprocessing.rst.txt -
Categorical Feature Support in Gradient Boostin...
for pipe in ( hist_dropped , hist_one_hot...hist_ordinal , hist_native ): if pipe is hist_native : # The native...scikit-learn.org/stable/auto_examples/ensemble/plot_gradient_boosting_categorical.html -
Selecting dimensionality reduction with Pipelin...
load_digits ( return_X_y = True ) pipe = Pipeline ( [ ( "scaling" ,...f)" ] grid = GridSearchCV ( pipe , n_jobs = 1 , param_grid =...scikit-learn.org/stable/auto_examples/compose/plot_compare_reduction.html