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clustering.rst.txt
lower scores, and for the adjusted Rand index the score will be...1.0 is the perfect match score. The score range is [0, 1] for the...scikit-learn.org/stable/_sources/modules/clustering.rst.txt -
Wikipedia principal eigenvector — scikit-...
prev_scores = scores scores = ( alpha * ( scores * X + np . dot...< n * tol : return scores return scores print ( "Computing...scikit-learn.org/stable/auto_examples/applications/wikipedia_principal_eigenvector.html -
HistGradientBoostingRegressor — scikit-le...
Scores are computed according to the scoring parameter....parameter. If scoring is not ‘loss’, scores are computed on a subset...scikit-learn.org/stable/modules/generated/sklearn.ensemble.HistGradientBoostingRegressor.html -
r2_score — scikit-learn 1.8.0 documentation
determination) regression score function. Best possible score is 1.0 and it...\(R^2\) score is identical to the Explained Variance score . Read...scikit-learn.org/stable/modules/generated/sklearn.metrics.r2_score.html -
davies_bouldin_score — scikit-learn 1.8.0...
Compute the Davies-Bouldin score. The score is defined as the average...result in a better score. The minimum score is zero, with lower...scikit-learn.org/stable/modules/generated/sklearn.metrics.davies_bouldin_score.html -
plot_multi_metric_evaluation.py
scoring=scoring, refit="AUC", n_jobs=2,...can be done by setting the ``scoring`` parameter to a list of metric...scikit-learn.org/stable/_downloads/dedbcc9464f3269f4f012f4bfc7d16da/plot_multi_metric_evaluation.py -
LassoCV — scikit-learn 1.8.0 documentation
Notes The \(R^2\) score used when calling score on a regressor uses...X , y ) >>> reg . score ( X , y ) 0.9993 >>>...scikit-learn.org/stable/modules/generated/sklearn.linear_model.LassoCV.html -
3. Model selection and evaluation — sciki...
Which scoring function should I use? 3.4.2. Scoring API overview...selection 3.1.5. Permutation test score 3.2. Tuning the hyper-parameters...scikit-learn.org/stable/model_selection.html -
11. Common pitfalls and recommended practices &...
accuracy: { scores . mean () : .2f } +/- { scores . std () : .2f...result in overly optimistic scores. Conversely, the transform...scikit-learn.org/stable/common_pitfalls.html -
Semi-supervised Classification on a Text Datase...
model performance and return F1 score""" print ( f...f " Micro-averaged F1 score on test set: { f1 : .3f } "...scikit-learn.org/stable/auto_examples/semi_supervised/plot_semi_supervised_newsgroups.html