Applied Data Science Coding with Python: How to get Regression Metric MAE

How to get Regression Metric MAE

Regression is a type of machine learning that is used to predict numerical values. One way to evaluate the performance of a regression model is to use a metric called Mean Absolute Error (MAE).

To calculate MAE, you first need to have a dataset with both input variables (also known as “features”) and a target variable (the value you are trying to predict). Once you have this dataset, you can use a software tool or programming library to fit a regression model to the data. This will generate predictions for the target variable based on the input variables.

Next, you compare these predictions to the actual target values in the dataset. To calculate MAE, you take the absolute value (i.e. remove any negative sign) of the difference between each prediction and the actual value, and then find the average of all these absolute differences. The smaller the MAE, the better the model is at making accurate predictions.

In summary, to get the MAE of a regression model, you need a dataset, a regression model and a way to compare the prediction with the actual value, then take the average of the absolute difference.


In this Applied Machine Learning & Data Science Recipe, the reader will learn: How to get Regression Metric MAE.


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