无法理解这个错误 "Unknown label type: 'continuous-multioutput' "

Question

我正在尝试运行数据集上的几种机器学习算法来预测 salary/income 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，其中包含 1% 样本、10% 样本和 100% 样本的不同大小的样本集。我收到未知错误“未知标签类型：'continuous-multioutput'” 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'

我希望此代码运行并显示这些算法针对此数据集的准确性和其他指标。

P.S。我知道代码太长太麻烦，但请努力完成它并让我知道解决方案。我是这个机器学习领域的新手。任何帮助将不胜感激。

P.S。请不要将此问题标记为重复我已经遇到过类似的问题并尝试了那里建议的所有内容但徒劳无功。它对我没有任何用处。

Answer 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

Answer 2

您收到未知错误“未知标签类型：'continuous-multioutput'”，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)

由于我们在训练和测试变量 x 和 y 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。

无法理解这个错误 "Unknown label type: 'continuous-multioutput' "

Can't understand this error "Unknown label type: 'continuous-multioutput' "

machine-learning

svm

decision-tree

python-3.x

pandas