ValueError：使用sklearn roc_auc_score函数不支持多类多输出格式

Question

我正在使用logistic regression进行预测。我的预测是0's和1's。在给定数据训练我的模型之后，以及在重要特征训练时，X_important_train看截图。我获得了大约70％的分数但是当我使用roc_auc_score(X,y)或roc_auc_score(X_important_train, y_train)时，我得到了价值错误：  ValueError: multiclass-multioutput format is not supported

代码：

# Load libraries
from sklearn.linear_model import LogisticRegression
from sklearn import datasets
from sklearn.preprocessing import StandardScaler
from sklearn.metrics import roc_auc_score

# Standarize features
scaler = StandardScaler()
X_std = scaler.fit_transform(X)

# Train the model using the training sets and check score
model.fit(X, y)
model.score(X, y)

model.fit(X_important_train, y_train)
model.score(X_important_train, y_train)

roc_auc_score(X_important_train, y_train)

截图：

Answer 1

首先，roc_auc_score函数需要具有相同形状的输入参数。

sklearn.metrics.roc_auc_score(y_true, y_score, average=’macro’, sample_weight=None)

Note: this implementation is restricted to the binary classification task or multilabel classification task in label indicator format.

y_true : array, shape = [n_samples] or [n_samples, n_classes]
True binary labels in binary label indicators.

y_score : array, shape = [n_samples] or [n_samples, n_classes]
Target scores, can either be probability estimates of the positive class, confidence values, or non-thresholded measure of decisions (as returned by “decision_function” on some classifiers).

现在，输入是真实和预测的分数，而不是您在发布的示例中使用的培训和标签数据。 更详细，

model.fit(X_important_train, y_train)
model.score(X_important_train, y_train)
# this is wrong here
roc_auc_score(X_important_train, y_train)

你应该这样：

y_pred = model.predict(X_test_data)
roc_auc_score(y_true, y_pred)