2、✌ 函数形式 sklearn.model_selection.learning_curve(estimator, X, y, groups=None, train_sizes=array([0.1, 0.33, 0.55, 0.78, 1. ]), cv=’warn’, scoring=None, exploit_incremental_learning=False, n_jobs=None, pre_dispatch=’all’, verbose=0, shuffle=False, random_state=None, error...
sklearn.model_selection.learning_curve(estimator, X, y, groups=None, train_sizes=array([0.1, 0.33, 0.55, 0.78, 1. ]), cv=’warn’, scoring=None, exploit_incremental_learning=False, n_jobs=None, pre_dispatch=’all’, verbose=0, shuffle=False, random_state=None, error_score=’raise-de...
sklearn.model_selection.learning_curve(estimator, X, y, groups=None, train_sizes=array([0.1, 0.33, 0.55, 0.78, 1. ]), cv=’warn’, scoring=None, exploit_incremental_learning=False, n_jobs=None, pre_dispatch=’all’, verbose=0, shuffle=False, random_state=None, error_score=’raise-de...
learning_curve(estimator, X, y, train_sizes=array([ 0.1 , 0.325, 0.55 , 0.775, 1. ]), cv=None, scoring=None, exploit_incremental_learning=False, n_jobs=1, pre_dispatch='all', verbose=0) 这个函数的作用为:对于不同大小的训练集,确定交叉验证训练和测试的分数。一个交叉验证发生器将整个数据...
train_sizes:训练样本相对的或绝对的数字,这些量的样本将会生成learning curve。 cv:确定交叉验证的分离策略(None:使用默认的3-fold cross-validation;integer:确定几折交叉验证) verbose:整型,可选择的。控制冗余:越高,有越多的信息。 返回值: train_sizes_abs:生成learning curve的训练集的样本数。重复的输入会被...
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learning_curve(estimator, X, y, train_sizes=array([ 0.1 , 0.325, 0.55 , 0.775, 1. ]), cv=None, scoring=None, exploit_incremental_learning=False, n_jobs=1, pre_dispatch='all', verbose=0) 这个函数的作用为:对于不同大小的训练集,确定交叉验证训练和测试的分数。一个交叉验证发生器将整个数据...
本文是对scikit-learn.org上函数说明<learning_curve>一文的翻译。 包括其引用的用户手册-learning_curve 函数签名Signature: learning_curve(estimator,X,y,*,groups=None,train_sizes=array([0.1,0.325,0.55,0.775,1.]),cv=None,scoring=None,exploit_incremental_learning=False,n_jobs=None,pre_dispatch='all'...
本文是对scikit-learn.org上函数说明<learning_curve>一文的翻译。 包括其引用的用户手册-learning_curve 函数签名Signature: 代码语言:javascript 复制 learning_curve(estimator,X,y,*,groups=None,train_sizes=array([0.1,0.325,0.55,0.775,1.]),cv=None,scoring=None,exploit_incremental_learning=False,n_jobs=...
train_sizes, train_loss, test_loss = learning_curve( SVC(gamma=0.001), X, y, cv=10, scoring='neg_mean_squared_error', train_sizes=[0.1, 0.25, 0.5, 0.75, 1]) #平均每一轮所得到的平均方差(共5轮,分别为样本10%、25%、50%、75%、100%) ...