Create a machine learning model of a real-life process or object : Adding More Metrics to Gain a Better Understanding

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.

What are some metrics that clients might want to know in addition to the mean absolute error?

How is the mean squared error calculated and what is its significance?

What steps are involved in implementing a custom metric if it is not available in TensorFlow?

What does an R-squared value indicate about a regression model?

What are the implications of an R-squared value below 0?

What is the relationship between the R-squared value and the quality of regression?

What strategies can be employed to improve the R-squared score of a model?