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Fess Configuration Properties
he=hen hi=hin hr=hrn hu=hun hy=hyn id=idn it=itn ja=jan ko=kon...sv=svn ta=tan te=ten th=thn tl=tln tr=trn uk=ukn ur=urn vi=vin...fess.codelibs.org/14.19/config/properties.html -
Comparing different clustering algorithms on to...
) # ========== # Create cluster objects # ========== ms = cluster...) # ========== # Set up cluster parameters # ========== plt ....scikit-learn.org/stable/auto_examples/cluster/plot_cluster_comparison.html -
Classification of text documents using sparse f...
name )) ========== Logistic Regression __________...dimensionality: 5316 density: 1.0 ========== Ridge Classifier __________...scikit-learn.org/stable/auto_examples/text/plot_document_classification_20newsgroups.html -
Combine predictors using stacking — scikit-lear...
(): df = fetch_openml ( name = "house_prices" , as_frame = True...True ) X = df . data y = df . target features = [ "YrSold" , "HeatingQC"...scikit-learn.org/stable/auto_examples/ensemble/plot_stack_predictors.html -
Post-tuning the decision threshold for cost-sen...
mask_false_negative = ( y_true == 1 ) & ( y_pred == 0 ) fraudulent_refuse = mask_true_positive...ax = axs [ 1 ], name = name , plot_chance_level = idx == 1 ,...scikit-learn.org/stable/auto_examples/model_selection/plot_cost_sensitive_learning.html -
Feature transformations with ensembles of trees...
max_depth = max_depth , random_state = 10 ) _ = gradient_boosting...RandomTreesEmbedding(max_depth=3, n_estimators=10, random_state=0)), ('logisticregression',...scikit-learn.org/stable/auto_examples/ensemble/plot_feature_transformation.html -
Displaying Pipelines — scikit-learn 1.6.1 docum...
default is display='diagram' . set_config ( display = "diagram" ) pipe...change to display='text' . set_config ( display = "text" ) pipe...scikit-learn.org/stable/auto_examples/miscellaneous/plot_pipeline_display.html -
Comparing Target Encoder with Other Encoders — ...
wine_reviews = fetch_openml ( data_id = 42074 , as_frame = True ) df...handle_unknown = "ignore" , max_categories = 20 , sparse_output = False...scikit-learn.org/stable/auto_examples/preprocessing/plot_target_encoder.html -
t-SNE: The effect of various perplexity values ...
factor = 0.5 , noise = 0.05 , random_state = 0 ) red = y == 0 green...green = y == 1 ax = subplots [ 0 ][ 0 ] ax . scatter ( X [ red...scikit-learn.org/stable/auto_examples/manifold/plot_t_sne_perplexity.html -
Comparing Random Forests and Histogram Gradient...
fig = make_subplots ( rows = 1 , cols = 2 , shared_yaxes = True...), legend = dict ( x = 0.72 , y = 0.05 , traceorder = "normal"...scikit-learn.org/stable/auto_examples/ensemble/plot_forest_hist_grad_boosting_comparison.html