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Logistic Regression is a statistical method that we use to fit a regression model when the response variable is binary.

To assess how well a logistic regression model fits a dataset, we can look at the following two metrics:

**Sensitivity:**The probability that the model predicts a positive outcome for an observation when indeed the outcome is positive. This is also called the “true positive rate.”**Specificity:**The probability that the model predicts a negative outcome for an observation when indeed the outcome is negative. This is also called the “true negative rate.”

One way to visualize these two metrics is by creating a **ROC curve**, which stands for “receiver operating characteristic” curve.

This is a plot that displays the sensitivity along the y-axis and (1 – specificity) along the x-axis.

One way to quantify how well the logistic regression model does at classifying data is to calculate **AUC**, which stands for “area under curve.”

The closer the AUC is to 1, the better the model.

The following step-by-step example shows how to calculate AUC for a logistic regression model in Python.

**Step 1: Import Packages**

First, we’ll import the packages necessary to perform logistic regression in Python:

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

**Step 2: Fit the Logistic Regression Model**

Next, we’ll import a dataset and fit a logistic regression model to it:

**#import dataset from CSV file on Github
url = "https://raw.githubusercontent.com/Statology/Python-Guides/main/default.csv"
data = pd.read_csv(url)
#define the predictor variables and the response variable
X = data[['student', 'balance', 'income']]
y = data['default']
#split the dataset into training (70%) and testing (30%) sets
X_train,X_test,y_train,y_test = train_test_split(X,y,test_size=0.3,random_state=0)
#instantiate the model
log_regression = LogisticRegression()
#fit the model using the training data
log_regression.fit(X_train,y_train)**

**Step 3: Calculate the AUC**

We can use the **metrics.roc_auc_score()** function to calculate the AUC of the model:

**#use model to predict probability that given y value is 1
y_pred_proba = log_regression.predict_proba(X_test)[::,1]
#calculate AUC of model
auc = metrics.roc_auc_score(y_test, y_pred_proba)
#print AUC score
print(auc)
0.5602104030579559
**

The AUC (area under curve) for this particular model is **0.5602**.

Recall that a model with an AUC score of **0.5** is no better than a model that performs random guessing.

Thus, in most cases a model with an AUC score of **0.5602** would be considered poor at classifying observations into the correct classes.

**Additional Resources**

The following tutorials offer additional information about ROC curves and AUC scores:

How to Interpret a ROC Curve (With Examples)

What is Considered a Good AUC Score?