DATA MINING AND STAT LEARN UPDATED ACTUAL EXAM QUESTIONS CORRECT
ANSWERS GRADED A PLUS
Question:
Forward Selection.
Answer:
Start with a model that has no factors. At each step we find the best new factor to add to the model
and put it in as long as it's a good enough improvement. When there's no factor that's good enough
to add, or if we've added as many factors as we want to have, we stop.
Question:
Backward Elimination.
Answer:
We start with a model that includes all factors and at each step, we find the worst factor and remove
it from the model. We continue until there's no factor bad enough to remove, and the model doesn't
have any more factors than we want.
Question:
Stepwise Regression.
Answer:
Multiple different forms, but essentially a combination of forward selection and backwards
elimination. Since in each step these models look at only the best current option and don't take
future possibilities into account it is known as the Greedy Algorithm
Question:
Lasso Approach.
Answer:
, We add a constraint to the standard regression equation. The goal is still to minimize SSE given the
regression a budget t to use on coefficients. It'll use that budget on the most important coefficients
which means all the rest of the factors will have zero coefficient and so those factors won't be part of
the model.
Question:
Elastic Net.
Answer:
Elastic net is effectively a combination of LASSO and Ridge Regressions that trades some bias in
order to reduce variance and ultimately reduce total prediction error. Constrains a combination of
the absolute value of the coefficients and their squares.
Question:
Elastic Net Pros/Cons.
Answer:
Pros: Variable selection benefits of LASSO Predictive Benefits of Ridge Regression Cons:
Arbitrarily rules out some correlated variables Underestimate coefficients of very predictive
variables
Question:
A/B Testing.
Answer:
Analytic method used to pick the best out of several alternatives. Best used when data can be
collected quickly, from a representative population, and the amount of data is small relative to the
whole population.
Question:
Factorial Design Tests.
Answer:
ANSWERS GRADED A PLUS
Question:
Forward Selection.
Answer:
Start with a model that has no factors. At each step we find the best new factor to add to the model
and put it in as long as it's a good enough improvement. When there's no factor that's good enough
to add, or if we've added as many factors as we want to have, we stop.
Question:
Backward Elimination.
Answer:
We start with a model that includes all factors and at each step, we find the worst factor and remove
it from the model. We continue until there's no factor bad enough to remove, and the model doesn't
have any more factors than we want.
Question:
Stepwise Regression.
Answer:
Multiple different forms, but essentially a combination of forward selection and backwards
elimination. Since in each step these models look at only the best current option and don't take
future possibilities into account it is known as the Greedy Algorithm
Question:
Lasso Approach.
Answer:
, We add a constraint to the standard regression equation. The goal is still to minimize SSE given the
regression a budget t to use on coefficients. It'll use that budget on the most important coefficients
which means all the rest of the factors will have zero coefficient and so those factors won't be part of
the model.
Question:
Elastic Net.
Answer:
Elastic net is effectively a combination of LASSO and Ridge Regressions that trades some bias in
order to reduce variance and ultimately reduce total prediction error. Constrains a combination of
the absolute value of the coefficients and their squares.
Question:
Elastic Net Pros/Cons.
Answer:
Pros: Variable selection benefits of LASSO Predictive Benefits of Ridge Regression Cons:
Arbitrarily rules out some correlated variables Underestimate coefficients of very predictive
variables
Question:
A/B Testing.
Answer:
Analytic method used to pick the best out of several alternatives. Best used when data can be
collected quickly, from a representative population, and the amount of data is small relative to the
whole population.
Question:
Factorial Design Tests.
Answer:
