To write python code to plot ROC curve used in ANN.

1. Hardware – PCs
2. Anaconda – Python 3.7 Installation / Moodle-Code Runner /Google Colab

The receiver operating characteristic (ROC) curve is frequently used for evaluating the performance of binary classification algorithms. It provides a graphical representation of a classifier’s performance, rather than a single value like most other metrics. First, let’s establish that in binary classification, there are four possible outcomes for a test prediction: true positive, false positive, true negative, and false negative. The ROC curve is produced by calculating and plotting the true positive rate against the false positive rate for a single classifier at a variety of thresholds.

One advantage presented by ROC curves is that they aid us in finding a classification threshold that suits our specific problem.

On the other hand, if our classifier is predicting whether someone has a terminal illness, we might be ok with a higher number of false positives (incorrectly diagnosing the illness), just to make sure that we don’t miss any true positives (people who actually have the illness).

1. Import Necessary Packages
2. Load the Data
3. Create Training and Test Samples
4. Fit the Logistic Regression Model
5. Model Diagnostics

/*
Program to plot Receiver Operating Characteristic [ROC] Curve.
Developed by   : P.SUGANYA
RegisterNumber :  212220230049
*/

import pandas as pd
import numpy as np
from sklearn import metrics 
from sklearn.linear_model import LogisticRegression 
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split

url = "https://raw.githubusercontent.com/Statology/Python-Guides/main/default.csv"
data = pd.read_csv(url)


x=data[['student','balance','income']]

y=data['default']
x_train,x_test,y_train,y_test,= train_test_split(x,y,test_size=0.3,random_state=0)
log_regression= LogisticRegression()
log_regression.fit(x_train,y_train)
#define metrics
y_pred_proba=log_regression.predict_proba(x_test)[::,1]
fpr,tpr, _ = metrics.roc_curve(y_test,y_pred_proba)

plt.plot(fpr,tpr)
plt.ylabel("True Positive Rate")
plt.xlabel("False Positive Rate")

plt.show()

#define metrics
y_pred_proba=log_regression.predict_proba(x_test)[::,1]
fpr,tpr, _ = metrics.roc_curve(y_test,y_pred_proba)
auc = metrics.roc_auc_score(y_test,y_pred_proba)


plt.plot(fpr,tpr, label="AUC" + str(auc))
plt.ylabel("True Positive Rate")
plt.xlabel("False Positive Rate")
plt.legend(loc=4)

plt.show()

Thus the python program successully plotted Receiver Operating Characteristic [ROC] Curve.