label_binarize 不适合显示错误输入形状的 sklearn 朴素贝叶斯分类器

Posted 2023-03-12

【中文标题】label_binarize 不适合显示错误输入形状的 sklearn 朴素贝叶斯分类器

【英文标题】：label_binarize Does not fit for sklearn Naive Bayes classifier showing bad input shape

【发布时间】：2019-12-08 12:01:44

【问题描述】：

我试图使用朴素贝叶斯为多类创建 roc 曲线但它以

结尾

ValueError：输入形状错误。

import numpy as np
import matplotlib.pyplot as plt
from itertools import cycle

from sklearn import svm, datasets
from sklearn.metrics import roc_curve, auc
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import label_binarize
from sklearn.naive_bayes import BernoulliNB

from scipy import interp

# Import some data to play with
iris = datasets.load_iris()
X = iris.data
y = iris.target

# Binarize the output
y = label_binarize(y, classes=[0, 1, 2])
n_classes = y.shape[1]

# Add noisy features to make the problem harder
random_state = np.random.RandomState(0)
n_samples, n_features = X.shape
X = np.c_[X, random_state.randn(n_samples, 200 * n_features)]

# shuffle and split training and test sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=.5,
                                                    random_state=0)

# Learn to predict each class against the other
classifier = BernoulliNB(alpha=1.0, binarize=6, class_prior=None, fit_prior=True)
y_score = classifier.fit(X_train, y_train).predict(X_test)

raise ValueError("bad input shape 0".format(shape))

ValueError: bad input shape (75, 6)

【问题讨论】：

哪一行？为什么六个班，Iris只有三个？！？

在我试图拟合分类器的最后一行。 y_score = classifier.fit(X_train, y_train).predict(X_test) 我什至尝试了 3 类但我得到了错误。

【参考方案1】：

由于二值化 y 变量而导致的错误。估算器本身可以使用字符串值。

删除以下行，

y = label_binarize(y, classes=[0, 1, 2])
n_classes = y.shape[1]

你可以走了！

要获得roc_curve 的预测概率，请使用以下命令：

classifier.fit(X_train, y_train)
y_score = classifier.predict_proba(X_test)
y_score.shape
# (75, 3)