Then later in type_of_target (in utils/multiclass.py) that array is classed as "unknown" type. I think the bug/place to change this is in BaseEstimator._validate_data() so that the dtype of the resulting array is np.int64, or something like that, instead of object. betatim removed ...
解决问题 raise ValueError("Unknown label type: %s" % repr(ys))ValueError: Unknown label type: (array([24 解决思路 错误:未知标签类型 解决方法 对当前特征变量进行声明或者定义数据类型即可,比如 df_y = df_y.astype('int') 即可!转藏 分享 献花(0) 来自...
The "Unknown label type: 'unknown'" error is typically encountered when working with the Y values in scikit-learn. The error arises due to a discrepancy between the expected input format and the actual data passed. This mismatch could be between Array and DataFrame or 1D list and 2D list,...
如果sklearn 导入的分类算法,即逻辑回归是在连续目标变量上训练的,它会抛出ValueError: Unknown label type:'continuous'。 代码: importnumpyasnpfromsklearn.linear_modelimportLogisticRegression input_var=np.array([[1.1,1.2,1.5,1.6],[0.5,0.9,0.6,0.8]]) target_var=np.array([1.4,0.4]) classifier_logis...
文件“C:\Anaconda3\lib\site-packages\sklearn\utils\multiclass.py”,第 172 行,>in check_classification_targets raise ValueError(“Unknown label type: %r” % y_type)ValueError:未知标签类型:“未知”来自sklearn 文档: http ://scikit-learn.org/stable/modules/generated/sklearn.linear_model.Logistic...
ValueError: Unknown label type: 'continuous' 1 说明:SVM训练的标签列必须为整型数值,不能为float.<br><br>y = np.array(y, dtype=int)<br><br>或<br><br>y.astype('int')
raise ValueError("Unknown label type: %r" % y_type)ValueError: Unknown label type: 'continuous' Essentially, the error is telling us that the type of the target variable is continuous which is not compatible with the specific model we are trying to fit (i.e. LogisticRegression).Getting...
If sklearn imported classification algorithm, i.e., Logistic Regression is trained on the continuous target variable, it throwsValueError: Unknown label type:'continuous'. Code: importnumpyasnpfromsklearn.linear_modelimportLogisticRegression input_var=np.array([[1.1,1.2,1.5,1.6],[0.5,0.9,0.6,0.8...
当我们在使用Python进行数值计算时，有时会遇到类似于ValueError: cannot convert float NaN to integer的错误。这个错误通常是由于我们试图将一个NaN（Not a Number）转换为整数类型引起的。在本篇文章中，我们将讨论这个错误的原因以及如何解决它。
你的标签[[0.11], [ 0.12],...。您应该使用ExtraTreesRegressor而不是ExtraTreesClassifier ...