With reasons, explain if following are examples of good or bad machine learning practice:
Ammar is solving a regression problem. He has a dataset of 500. He splits the dataset into training (60%), validation (20%) and testing (20%). Ammar fits multiple models on the dataset. The quadratic and cubic models are very close in their validation errors with cubic having smaller validation error so Ammar chooses the cubic model.
Yes it is a good machine learning practice for the following with some reservations
Fitting the curves means you need to increase the degree of your polynomial equations. Which means now:
Y = mX + c will be changed to something like
Y = m1X1 + m2X^2 + m3X^3 +m4X^4 ...... + mnX^n
So Y = mX + c is linear model
Y = m1X^1 + m2X^2 is squared model
Y = m1X^1 + m2X^2 + m3X^3 is a cubic model and so on.
So consider an assumption duing linear model amaar got 95% accuracy, in square he got 96% accuracy in cubic model he got 99% accuracy. But after cubic model there no change in accuracy even after increasing the degree of independent variables to fit the curve. Hence he used cubic model.
With reasons, explain if following are examples of good or bad machine learning practice: Ammar is...
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