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

Mean Absolute Error (MAE) is a metric used to evaluate the performance of a regression model. It is a measure of the average difference between the predicted values and the true values. MAE is calculated as the sum of the absolute differences between the predicted and true values, divided by the number of examples.

To calculate the MAE in Python, we first need to split the data into training and testing sets. This is done using the "train_test_split" function from the "scikit-learn" library. The function takes in the data, as well as the percentage of the data that should be used for testing, and returns the training and testing sets.

Once the data is split, the model is trained on the training set using the "fit" method. This method takes in the training data and the model parameters. After the model is trained, it is used to make predictions on the test set using the "predict" method.

To calculate the MAE, we use the "mean_absolute_error" function from the "scikit-learn" library. This function takes in the true values and the predicted values, and returns the MAE as a value between 0 and infinity. Lower values of MAE indicate that the model's predictions are closer to the true values and hence the model has a better performance.

Additionally, it's also important to repeat this process multiple times (with different random splits) and average the results to get a more robust estimate of the model's performance. This is known as k-fold cross validation.

It's important to note that MAE is sensitive to outliers, if the data has large values they will greatly affect the result. In contrast, Mean Squared Error (MSE) is less sensitive to outliers.

In summary, to calculate the MAE in Python, we first split the data into training and testing sets, train the model on the training set, make predictions on the test set, and use the "mean_absolute_error" function to calculate the MAE. MAE is a simple and easy to understand metric that measures the average difference between the predicted values and the true values. Additionally, it's important to repeat this process multiple times and average the results to get a more robust estimate of the model's performance.

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