无法在逻辑回归中将字符串转换为浮点数

Question

我写了以下代码：

from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score

Spam_model = LogisticRegression(solver='liblinear', penalty='l1')
print(X_train)

Spam_model.fit(X_train, Y_train)
pred = Spam_model.predict(X_test)
accuracy_score(Y_test,pred)

它抛出以下错误。这可能是什么原因？

Answer 1

逻辑回归适用于数字，而不是字符串。您输入一个值（或更多），它会预测另一个值。浮点数是带小数的数字。例如，2 是整数，2.53 是浮点数。你能做的是

a = '0.67687980'
print(float(a))

返回

0.67687980

但是，你不能用字符串来做

a = 'Some string'
print(float(a))

返回时：

ValueError: could not convert string to float: 'Some string'

如果您使用的数据不是数字，则应先将其全部转换为数字以避免此错误

Answer 2

如果你有文本作为数据，你需要在应用分类器之前做feature extraction。使用来自 sklearn 的 old example：

from sklearn.datasets import fetch_20newsgroups
cats = ['alt.atheism', 'sci.space']

newsgroups_train = fetch_20newsgroups(subset='train', categories=cats)
X_train = newsgroups_train.data
Y_train = newsgroups_train.target

newsgroups_test = fetch_20newsgroups(subset='test', categories=cats)
X_test = newsgroups_test.data
Y_test = newsgroups_test.target
 

数据如下所示：

Y_train
array([0, 1, 1, ..., 1, 1, 1])

X_train[0][:50]
'From: bil@okcforum.osrhe.edu (Bill Conner)\nSubject'

应用矢量化器将文本转换为基本的数字特征，然后训练模型：

from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score

vectorizer = TfidfVectorizer()
X_train_vec = vectorizer.fit_transform(X_train)
X_test_vec = vectorizer.transform(X_test)

model = LogisticRegression(solver='liblinear', penalty='l1')

model.fit(X_train_vec, Y_train)
pred = model.predict(X_test_vec)
accuracy_score(Y_test,pred)

0.906030855539972