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.

Why might clients require metrics other than mean absolute error?

• To simplify the model

• To reduce computational cost

• To fit their specific use cases

• To better understand classification tasks

Which metric can be added to a TensorFlow model to measure regression performance?

• Binary accuracy

• Mean squared error

• Categorical crossentropy

• Precision

What is the range of values for the R-squared metric?

• -1 to 1

• 0 to 1

• -1 to 0

• 0 to 100

What does an R-squared value below 0 indicate?

• The model is overfitting

• The model is perfect

• The model is worse than random predictions

• The model is performing well

How is the residual calculated in the R-squared metric implementation?

• Sum of differences between predictions and mean of true values

• Mean of squared differences

• Sum of squared differences between predictions and true values

• Sum of absolute differences

What is suggested to improve the R-squared score?

• Decrease the number of epochs

• Use a smaller neural network

• Tune activation functions and run for more epochs

• Reduce the dataset size

What is the goal for the R-squared score mentioned in the video?

• To be at least 0.3

• To reach exactly 0.5

• To be negative

• To be exactly 1