无法理解此错误“未知标签类型：'连续多输出'”答案

【问题标题】：Can't understand this error "Unknown label type: 'continuous-multioutput' "无法理解此错误“未知标签类型：'连续多输出'”
【发布时间】：2019-10-16 05:15:03
【问题描述】：

我正在尝试在数据集上运行几种机器学习算法来预测工资/收入是大于 50k 还是小于等于 50k。我创建了一个函数并将值传递给它，具有不同大小的 1% 样本、10% 样本和 100% 样本的样本集。我收到一个未知错误'未知标签类型：'连续多输出'' 我不知道这个错误是什么。

我尝试更改我使用的分类算法但没有用。它对所有算法都显示相同的错误。

from sklearn.metrics import fbeta_score, accuracy_score

def train_predict (learner, sample_size, X_train, X_test, y_train, y_test):
    results = {}
    start = time()
    learner = learner.fit(X_train[:sample_size], y_train)
    end = time()
    results['train_time'] = end - start

    start = time()
    predictions_test = learner.predict(X_test)
    predictions_train = learner.predict(X_train[:sample_size])
    end = time()

    results['pred_time'] = end - start
    results['acc_train'] = accuracy_score(X_train[:sample_size], y_train[:sample_size])
    results['acc_test'] = accuracy_score[X_test, y_test]
    results['f_train'] = fbeta_score(X_train[:sample_size], y_train[:sample_size], beta = 1)
    resutts['f_test'] = fbeta_score(X_test, y_test, beta = 1)
    print("{} trained on {} samples. ".format(learner.__class__.__name__, sample_size))
    return results

from sklearn.tree import DecisionTreeClassifier
from sklearn.naive_bayes import GaussianNB
from sklearn.svm import SVC

clf_A = DecisionTreeClassifier()
clf_B = GaussianNB()
clf_C = SVC()

samples_100 = len(X_train)       #taking 100% i.e. all the data in training set
samples_10 = int(len(X_train)*.1) # taking 10% of the training data
samples_1 = int(len(X_train)*.01) #taking 1% of the training data

results= {}
for clf in [clf_A, clf_B, clf_C]:
    clf_name = clf.__class__.__name__
    results[clf_name] = {}
    for i, samples in enumerate([samples_1, samples_10, samples_100]):
        results[clf_name][i] = \
        train_predict(clf, samples, X_train, y_train, X_test, y_test)
vs.evaluate(results, accuracy, fscore)

---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
<ipython-input-66-e06d42fbd15b> in <module>
      5     for i, samples in enumerate([samples_1, samples_10, samples_100]):
      6         results[clf_name][i] = \
----> 7         train_predict(clf, samples, X_train, y_train, X_test, y_test)
      8 vs.evaluate(results, accuracy, fscore)

<ipython-input-62-4484b803a707> in train_predict(learner, sample_size, X_train, X_test, y_train, y_test)
      2     results = {}
      3     start = time()
----> 4     learner = learner.fit(X_train[:sample_size], y_train)
      5     end = time()
      6     results['train_time'] = end - start

C:\ProgramData\Anaconda3\lib\site-packages\sklearn\tree\tree.py in fit(self, X, y, sample_weight, check_input, X_idx_sorted)
    799             sample_weight=sample_weight,
    800             check_input=check_input,
--> 801             X_idx_sorted=X_idx_sorted)
    802         return self
    803 

C:\ProgramData\Anaconda3\lib\site-packages\sklearn\tree\tree.py in fit(self, X, y, sample_weight, check_input, X_idx_sorted)
    138 
    139         if is_classification:
--> 140             check_classification_targets(y)
    141             y = np.copy(y)
    142 

C:\ProgramData\Anaconda3\lib\site-packages\sklearn\utils\multiclass.py in check_classification_targets(y)
    169     if y_type not in ['binary', 'multiclass', 'multiclass-multioutput',
    170                       'multilabel-indicator', 'multilabel-sequences']:
--> 171         raise ValueError("Unknown label type: %r" % y_type)
    172 
    173 

ValueError: Unknown label type: 'continuous-multioutput'

我希望这段代码能够运行并显示这些算法对于这个数据集的准确性和其他指标。

附：我知道代码太长太麻烦，但请努力通过它，让我知道解决方案。我是这个机器学习领域的新手。任何帮助将不胜感激。

附：请不要将此问题标记为重复我已经经历过类似的问题并尝试了那里提出的所有建议但徒劳无功。它对我没有任何用处。

【问题讨论】：

请出示您的标签 y_train样本
X_train 包含 100 多个特征，而 y_train 是单个字符串，即“50k”。在这里，我试图从教育、职业、婚姻状况等多个特征来预测收入是大于 5 万还是小于 5 万。

标签： python-3.x pandas machine-learning svm decision-tree

【解决方案1】：

好的，我得到了错误，问题出在我对数据集进行采样的方式上。我使用 frac 属性更改了采样代码，错误消失了。

samples_100 = df.sample(frac = 1)      #taking 100% i.e. all the data in training set
samples_10 = df.sample(frac = .1)      #taking 10% of the training data
samples_1 = df.sample(frac = .01)      #taking 1% of the training data

【讨论】：

遗憾的是，这仍然无法解释错误的来源。

【解决方案2】：

您收到未知错误“未知标签类型：“连续多输出”，因为如果您在代码中看到

train_predict(clf, samples, X_train, y_train, X_test, y_test)

这需要重新排列为

train_predict(clf, samples, X_train, X_test, y_train, y_test)

由于我们在变量 x 和 y 的训练和测试之间拆分数据，因此顺序对于正确拆分和分配数据很重要。

我遇到了同样的问题，它对我有用。

【讨论】：