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?

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

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

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

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

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

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

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