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User Guide — scikit-learn 1.7.0 documentation
Guide # 1. Supervised learning 1.1. Linear Models 1.1.1. Ordinary...Lasso 1.1.4. Multi-task Lasso 1.1.5. Elastic-Net 1.1.6. Multi-task...scikit-learn.org/stable/user_guide.html -
Monotonic Constraints — scikit-learn 1.7.0 docu...
monotonic_cst = { "f_0" : 1 , "f_1" : - 1 } ) . fit ( X_df , y )...) f_1 = rng . rand ( n_samples ) X = np . c_ [ f_0 , f_1 ] noise...scikit-learn.org/stable/auto_examples/ensemble/plot_monotonic_constraints.html -
adjusted_rand_score — scikit-learn 1.7.0 docume...
1 , 1 ], [ 0 , 0 , 1 , 1 ]) 1.0 >>> adjusted_rand_score...adjusted_rand_score ([ 0 , 0 , 1 , 1 ], [ 1 , 1 , 0 , 0 ]) 1.0 Labelings that...scikit-learn.org/stable/modules/generated/sklearn.metrics.adjusted_rand_score.html -
class_likelihood_ratios — scikit-learn 1.7.0 do...
1 , 0 , 1 , 0 ], [ 1 , 1 , 0 , 0 , 0 ]) (1.5, 0.75)...LR+ ranges from 1.0 to infinity. A LR+ of 1.0 indicates that...scikit-learn.org/stable/modules/generated/sklearn.metrics.class_likelihood_ratios.html -
Simple 1D Kernel Density Estimation — scikit-le...
1 , int ( 0.3 * N )), np . random . normal ( 5 , 1 , int...score_samples ( X_plot ) ax [ 1 , 1 ] . fill ( X_plot [:, 0 ], np...scikit-learn.org/stable/auto_examples/neighbors/plot_kde_1d.html -
compute_optics_graph — scikit-learn 1.7.0 docum...
1. , 1. , 4.12]) >>> reachability array([ inf, 3.16, 1.41,...1.41, 4.12, 1. , 5. ]) >>> predecessor array([-1, 0, 1, 5, 3, 2])...scikit-learn.org/stable/modules/generated/sklearn.cluster.compute_optics_graph.html -
3. Model selection and evaluation — scikit-lear...
1.1. Computing cross-validated metrics 3.1.2. Cross...validation iterators 3.1.3. A note on shuffling 3.1.4. Cross validation...scikit-learn.org/stable/model_selection.html -
Plot Hierarchical Clustering Dendrogram — sciki...
n_samples : current_count += 1 # leaf node else : current_count...scikit-learn.org/stable/auto_examples/cluster/plot_agglomerative_dendrogram.html -
d2_tweedie_score — scikit-learn 1.7.0 documenta...
1 , 2.5 , 7 ] >>> y_pred = [ 1 , 1 , 5 , 3.5 ] >>>...explained. Best possible score is 1.0 and it can be negative (because...scikit-learn.org/stable/modules/generated/sklearn.metrics.d2_tweedie_score.html -
cluster_optics_xi — scikit-learn 1.7.0 document...
1, 1, 1]) >>> clusters array([[0, 2],...min_samples int > 1 or float between 0 and 1 The same as the min_samples...scikit-learn.org/stable/modules/generated/sklearn.cluster.cluster_optics_xi.html