ISYE 6501 MIDTERM EVALUATION SCRIPT
GRADED A+ SOLVED SOLUTIONS
◉ 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
◉ 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.
◉ 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.
◉ 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
◉ 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.
◉ Factorial Design Tests
Answer: Design of experiment method used to test a multitude of
combinations. E.g 2 fonts x 2 wordings x 2 backgrounds to
compare efficacy
◉ multi-armed bandit
Answer: Design of experiment approach that starts with no
information and k alternatives. Over time, while performing tests,
the probability of which alternative is best is updated and we
become more likely to chose the better alternatives in testing. This
approach helps balance exploration vs exploitation so less value is
lost testing suboptimal alternatives.
◉ Bernoulli Distribution
GRADED A+ SOLVED SOLUTIONS
◉ 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
◉ 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.
◉ 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.
◉ 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
◉ 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.
◉ Factorial Design Tests
Answer: Design of experiment method used to test a multitude of
combinations. E.g 2 fonts x 2 wordings x 2 backgrounds to
compare efficacy
◉ multi-armed bandit
Answer: Design of experiment approach that starts with no
information and k alternatives. Over time, while performing tests,
the probability of which alternative is best is updated and we
become more likely to chose the better alternatives in testing. This
approach helps balance exploration vs exploitation so less value is
lost testing suboptimal alternatives.
◉ Bernoulli Distribution
