嗨,我想将训练/测试拆分与交叉验证相结合,并在 auc 中获取结果。
我的第一个方法我明白了,但很准确。
# split data into train+validation set and test set
X_trainval, X_test, y_trainval, y_test = train_test_split(dataset.data, dataset.target)
# split train+validation set into training and validation sets
X_train, X_valid, y_train, y_valid = train_test_split(X_trainval, y_trainval)
# train on classifier
clf.fit(X_train, y_train)
# evaluate the classifier on the test set
score = svm.score(X_valid, y_valid)
# combined training & validation set and evaluate it on the test set
clf.fit(X_trainval, y_trainval)
test_score = svm.score(X_test, y_test)
而且我找不到如何申请roc_auc,请帮忙。
使用 scikit-learn 你可以做到:
import numpy as np
from sklearn import metrics
y = np.array([1, 1, 2, 2])
scores = np.array([0.1, 0.4, 0.35, 0.8])
fpr, tpr, thresholds = metrics.roc_curve(y, scores, pos_label=2)
现在我们得到:
print(fpr)
array([ 0. , 0.5, 0.5, 1. ])
print(tpr)
array([ 0.5, 0.5, 1. , 1. ])
print(thresholds)
数组([ 0.8 , 0.4 , 0.35, 0.1 ])
在代码中,在训练分类器后,使用以下方法获取预测:
y_preds = clf.predict(X_test)
然后使用它来计算 auc 值:
from sklearn.metrics import roc_curve, auc
fpr, tpr, thresholds = roc_curve(y, y_preds, pos_label=1)
auc_roc = auc(fpr, tpr)