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 dataset
df = pd.read_csv('fraud_prediction.csv')
#Define the feature and target arrays
feature = df['oldbalanceOrg'].values
target = df['amount'].values
#Initializing a linear regression model
linear_reg = linear_model.LinearRegression()
#Reshaping the array since we only have a single feature
feature = feature...