传递到sklearn.model_selection.cross_validate时，从DataFrame中选择的要素是否具有不同的长度？

Question

当我使用：

df_train[df_train.columns.drop('Survived')]

它会返回一个891*17数据帧。但是当我想用df_train['Survived']提取标签时，长度变为892。

这怎么会发生呢？数据帧中的所有列都具有相同的长度。当我尝试用dict(df_train)将我的数据帧转换成字典时，它还告诉我，所有列都有891个值，但'Survived‘有982个值。

我在收到错误时偶然发现了这一点。

ValueError: Found input variables with inconsistent numbers of samples: [891, 892]

尝试调用此函数时：

cv_results = model_selection.cross_validate(alg, df_train[df_train.columns.drop('Survived')], df_train['Survived'], cv=cv_split)

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编辑：

我在一个循环中运行多个MLA，如下所示：(就在此代码位之前，长度相等)

MLA_predict = df_train['Survived']

cv_split = model_selection.ShuffleSplit(n_splits = 10, test_size = .3, train_size = .6, random_state = 0 ) 
row_index = 0
for alg in MLA:

    #set name and parameters
    MLA_name = alg.__class__.__name__
    MLA_compare.loc[row_index, 'MLA Name'] = MLA_name
    MLA_compare.loc[row_index, 'MLA Parameters'] = str(alg.get_params())

    #score model with cross validation: http://scikit-learn.org/stable/modules/generated/sklearn.model_selection.cross_validate.html#sklearn.model_selection.cross_validate
    cv_results = model_selection.cross_validate(alg, df_train[df_train.columns.drop('Survived')], df_train['Survived'], cv=cv_split)

    MLA_compare.loc[row_index, 'MLA Time'] = cv_results['fit_time'].mean()
    MLA_compare.loc[row_index, 'MLA Train Accuracy Mean'] = cv_results['train_score'].mean()
    MLA_compare.loc[row_index, 'MLA Test Accuracy Mean'] = cv_results['test_score'].mean()   
    #if this is a non-bias random sample, then +/-3 standard deviations (std) from the mean, should statistically capture 99.7% of the subsets
    MLA_compare.loc[row_index, 'MLA Test Accuracy 3*STD'] = cv_results['test_score'].std()*3   #let's know the worst that can happen!


    #save MLA predictions - see section 6 for usage
    alg.fit(df_train[df_train.columns.drop('Survived')], df_train['Survived'])
    MLA_predict[MLA_name] = alg.predict(df_train[df_train.columns.drop('Survived')])

    row_index+=1

Answer 1

MLA_predict需要是一个不同的对象！

MLA_predict = df_train['Survived'].copy()