Python Machine Learning for Beginners: Learning from Scratch Numpy, Pandas, Matplotlib, Seaborn, SKlearn and TensorFlow 2.0 for Machine Learning & Deep Learning- With Exercises and Hands-on Projects by Publishing AI

Author:Publishing, AI [Publishing, AI]
Language: eng
Format: epub
Publisher: AI Publishing LLC
Published: 2020-10-24T16:00:00+00:00

1. from sklearn.linear_model import LinearRegression

2. # training the algorithm

3. lin_reg = LinearRegression()

4. regressor = lin_reg.fit(X_train, y_train)

5. # making predictions on test set

6. y_pred = regressor.predict(X_test)

Once you have trained a model and have made predictions on the test set, the next step is to know how well has your model performed for making predictions on the unknown test set. There are various metrics to check that. However, mean absolute error, mean squared error, and root mean squared error are three of the most common metrics.

Mean Absolute Error

Mean absolute error (MAE) is calculated by taking the average of absolute error obtained by subtracting real values from predicted values. The equation for calculating MAE is:

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