Machine Learning with scikit-learn Quick Start Guide

Performance evaluation for regression algorithms

There are three main metrics that you can use to evaluate the performance of the regression algorithm that you built, as follows:

• Mean absolute error (MAE)
• Mean squared error (MSE)
• Root mean squared error (RMSE)

In this section, you will learn what the three metrics are, how they work, and how you can implement them using scikit-learn. The first step is to build the linear regression algorithm. We can do this by using the following code:

## Building a simple linear regression model#Reading in the datasetdf = pd.read_csv('fraud_prediction.csv')#Define the feature and target arraysfeature = df['oldbalanceOrg'].valuestarget = df['amount'].values#Initializing a linear regression model linear_reg ...