Cross_val_score scoring r2、Cross_validate、Cross validation在PTT/mobile01評價與討論，在ptt社群跟網路上大家這樣說
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Cross_val_score scoring r2在sklearn.model_selection.cross_val_score的討論與評價
sklearn.model_selection.cross_val_score(estimator, X, y=None, *, groups=None, scoring=None, cv=None, n_jobs=None, verbose=0, fit_params=None, ...
Cross_val_score scoring r2在cross_val_score的scoring参数值解析 - CSDN博客的討論與評價
sklearn.model_selection. cross_val_score (estimator, X, y=None, groups=None, scoring=None, cv=None, n_jobs=1, verbose=0, fit_params=None, ...
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本文整理匯總了Python中sklearn.cross_validation.cross_val_score方法的典型用法 ... X, y, scoring=scoring_micro, cv=5) score_macro = cval.cross_val_score(clf, ...
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Cross_val_score scoring r2在[Day29]機器學習：交叉驗證！ - iT 邦幫忙的討論與評價
from sklearn.cross_validation import cross_val_score ... scores = cross_val_score(knn,X,y,cv=5,scoring='accuracy') print(scores) print(scores.mean()).
Cross_val_score scoring r2在Evaluate multiple scores on sklearn cross_val_score - Stack ...的討論與評價
Since the time of writing this post scikit-learn has updated and made my answer obsolete, see the much cleaner solution below ...
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“cross_val_score scoring options” Code Answer's. cross val score scoring options. python by Darkstar on Aug 17 2021 Comment. 0.
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... "precision", "recall"]: print score, print " : ", print cross_val_score(model, iris.data, iris.target,scoring=score, cv=10).mean() print ...
Cross_val_score scoring r2在Understanding cross_val_score | Data Science and Machine ...的討論與評價
from sklearn.model_selection import cross_val_score cv_results = cross_val_score(logreg, X, y, cv=5, scoring='accuracy'). And my output was:
Cross_val_score scoring r2在Python sklearn.cross_validation 模块，cross_val_score() 实例 ...的討論與評價
Get scores through cross validation score_f1 = cross_val_score(model, X, y, scoring='f1', cv=splitter_) score_pr = cross_val_score(model, X, y, ...
Cross_val_score scoring r2在Python Examples of sklearn.model_selection.cross_val_score的討論與評價
MEOWAFactory()) from sklearn.model_selection import cross_val_score scores = cross_val_score(l, X, y, cv=10) mean = np.mean(scores) assert 0.80 < mean.