Python Logistic Regression with ROC Curve and AUC Calculation

This code demonstrates how to train a Logistic Regression model on a crime dataset, calculate the ROC curve and AUC value, and finally plot the ROC curve. It includes data loading, splitting into train and test sets, model fitting, prediction, ROC curve calculation, and AUC value calculation.

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from sklearn.metrics import roc_curve, auc
from sklearn.model_selection import train_test_split
# 读取数据
data = pd.read_csv('mdcrime.csv')

# 划分训练集和测试集
from sklearn.linear_model import LogisticRegression
X=data.iloc[:,3:4]
y=data.iloc[:,-1]
X_train,X_test,y_train,y_test=train_test_split(X,y,test_size=0.3,random_state=10)
clf = LogisticRegression()
clf.fit(X_train, y_train)
probas_ = clf.predict_proba(X_test)

# 计算ROC曲线和AUC值
fpr, tpr, thresholds = roc_curve(y_test, probas_[:, -1])
roc_auc = auc(fpr, tpr)

# 绘制ROC曲线
plt.figure(figsize=(8, 6))
lw = 2
plt.plot(fpr, tpr, color='darkorange',
         lw=lw, label='ROC curve (area = %0.2f)' % roc_auc)
plt.plot([0, 1], [0, 1], color='navy', lw=lw, linestyle='--')
plt.xlim([0.0, 1.0])
plt.ylim([0.0, 2])
plt.xlabel('False Positive Rate')
plt.ylabel('True Positive Rate')
plt.title('Receiver Operating Characteristic')
plt.legend(loc="lower right")
plt.show()

Explanation:

1. Data Loading: The code starts by reading the crime dataset from a file named 'mdcrime.csv' using pd.read_csv().

2. Data Splitting: The dataset is then split into training and testing sets using train_test_split() with a test size of 30% and a random state of 10 for reproducibility.

3. Model Training: A Logistic Regression model is created using LogisticRegression() and trained on the training data (X_train, y_train) using the fit() method.

4. Prediction: The model predicts probabilities for the test data using predict_proba().

5. ROC Curve and AUC Calculation: The roc_curve() function calculates the false positive rate (fpr), true positive rate (tpr), and thresholds for different probability thresholds. The auc() function calculates the area under the curve (AUC) for the ROC curve.

6. ROC Curve Plotting: The code then plots the ROC curve using plt.plot() with the calculated fpr, tpr, and AUC value. The plot also includes a diagonal line representing the random classifier and labels for the axes and legend.

This code provides a basic framework for understanding and implementing Logistic Regression with ROC curve and AUC evaluation in Python.