Logistic regression in python Erorr: ValueError: could not convert string to float: 'concavity_worst'

Question

我正在尝试为如下所示的数据集制作一个简单的逻辑回归程序：https://imgur.com/a/dJvb8Si

我的程序应该对数据集使用逻辑回归，并输出一些关于回归结果的信息。使用示例我编写了以下代码：

import matplotlib.pyplot as plt
from scipy import stats

import pandas as pd
col_names = ['id', 'diagnosis', 'radius_mean', 'texture_mean', 'perimeter_mean', 'area_mean', 'smoothness_mean','compactness_mean', 'symmetry_se', 'perimeter_worst', 'smoothness_worst', 'concavity_worst']

# load dataset
data = pd.read_csv("DatasetTest.csv", header=None, names=col_names)
data.head()

feature_cols = ['diagnosis', 'radius_mean','texture_mean','perimeter_mean','area_mean', 'smoothness_mean','compactness_mean', 'symmetry_se', 'perimeter_worst','smoothness_worst', 'concavity_worst']
X = data[feature_cols]
y = data.diagnosis

from sklearn.model_selection import train_test_split
X_train,X_test,y_train,y_test=train_test_split(X,y,test_size=0.25,random_state=0)

from sklearn.linear_model import LogisticRegression
logreg = LogisticRegression()
logreg.fit(X_train,y_train)
y_pred=logreg.predict(X_test)

from sklearn import metrics
cnf_matrix = metrics.confusion_matrix(y_test, y_pred)
cnf_matrix

当运行代码出现此错误：

could not convert string to float: 'concavity_worst'

在 Whosebug 上找不到类似的问题。经过研究，我发现函数 fit() 显然不能接受字符串。但我不确定如何将字符串转换为浮点数。经过一些谷歌搜索后，我仍然找不到适用于这种情况的解决方案。

代码示例：https://towardsdatascience.com/a-beginners-guide-to-linear-regression-in-python-with-scikit-learn-83a8f7ae2b4f

Answer 1

根据pd.read_csv的pandasdocumentation，您需要

Explicitly pass header=0 to be able to replace existing names

如果不这样做，它会将文件头作为数据的一部分。所以，现在您所有的列名都与您的数据混合在一起，并且您的所有列都包含一个字符串。这将使回归崩溃，因为它不能将字符串作为输入。

Logistic regression in python Erorr: ValueError: could not convert string to float: 'concavity_worst'

Logistic regression in python Erorr: ValueError: could not convert string to float: 'concavity_worst'

python

logistic-regression

data-science