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minmax_scale — scikit-learn 1.8.0 documen...
minmax_scale >>> X = [[ - 2 , 1 , 2 ], [ - 1 , 0 , 1 ]] >>>...scikit-learn.org/stable/modules/generated/sklearn.preprocessing.minmax_scale.html -
make_swiss_roll — scikit-learn 1.8.0 docu...
2 ) S. Marsland, “Machine Learning:...scikit-learn.org/stable/modules/generated/sklearn.datasets.make_swiss_roll.html -
Nearest Centroid Classification — scikit-...
2 0.82 # Authors: The scikit-learn...dataset X = iris . data [:, : 2 ] y = iris . target # Create color...scikit-learn.org/stable/auto_examples/neighbors/plot_nearest_centroid.html -
Decision Boundaries of Multinomial and One-vs-R...
2 ], [ - 0.4 , 1.2 ]] X = np . dot ( X ,...1" , ylabel = "Feature 2" ) _ = ax . legend ( * scatter...scikit-learn.org/stable/auto_examples/linear_model/plot_logistic_multinomial.html -
Enhance user experiences with a multiturn conve...
Variable 2 - Res : This is the LLM response...Initial_query , query_text . Action 2: Generate answer Generate answer:...developer.ibm.com/articles/awb-enhance-ux-with-a-multi-turn-conversational-chatbot/ -
Procédure de mise à niveau
2/fess-15.3.2.tar.gz $ tar -xzf fess-15.3.2.tar.gz Copiez...-Uvh fess-15.3.2.rpm # DEB $ sudo dpkg -i fess-15.3.2.deb Note Les...fess.codelibs.org/fr/15.3/install/upgrade.html -
Comparing Random Forests and Histogram Gradient...
col = 2 ) fig . add_trace ( line_trace , row = 1 , col = 2 ) fig..." ) Number of physical cores: 2 Unlike RF, HGBT models offer an...scikit-learn.org/stable/auto_examples/ensemble/plot_forest_hist_grad_boosting_comparison.html -
Map data to a normal distribution — sciki...
figaspect ( 2 )) axes = axes . flatten () axes_idxs..., 9 ), ( 1 , 4 , 7 , 10 ), ( 2 , 5 , 8 , 11 ), ( 12 , 15 , 18...scikit-learn.org/stable/auto_examples/preprocessing/plot_map_data_to_normal.html -
bootstrap.js
A=v/2-y/2,T=m[_],C=E-g[p]-m[b],O=E/2-g[p]/2+A,x=X(T,O,...x+i.width/2-a.width/2,p=i.y+i.height/2-a.height/2;switch(d){case...scikit-learn.org/stable/_static/scripts/bootstrap.js -
Normalizer — scikit-learn 1.8.0 documenta...
2 , 2 ], ... [ 1 , 3 , 9 , 3 ], ......transform ( X ) array([[0.8, 0.2, 0.4, 0.4], [0.1, 0.3, 0.9, 0.3],...scikit-learn.org/stable/modules/generated/sklearn.preprocessing.Normalizer.html