The video tutorial discusses the importance of using various metrics to evaluate machine learning models, beyond just the mean absolute error. It introduces the mean squared error and R-squared metrics, explaining how to implement them in TensorFlow using Keras. The tutorial also covers the significance of the R-squared metric in regression tasks, detailing its calculation and interpretation. Finally, it suggests ways to improve model performance, such as tuning the neural network and adjusting activation functions, to achieve a better R-squared score.

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