Interaction (definition) - answer-Effect of one variable on target variable depends on the value/level of another variable.
GLM Description (idea) - answer-GLMs relate a function of target mean linearly to a set of predictors. Target itself is not transformed.
Recursive binary splitting (idea...
Predictive Analytics Exam
Interaction (definition) - answer-Effect of one variable on target variable depends on the
value/level of another variable.
GLM Description (idea) - answer-GLMs relate a function of target mean linearly to a set
of predictors. Target itself is not transformed.
Recursive binary splitting (idea) - answer-divide feature space recursively into a set of
non-overlapping regions of relatively homogeneous observations until a stopping
criterion is reached.
Random Forests (idea) - answer-Reduce overfitting and variance of base trees by
combining the results of multiple trees fitted to different bootstrapped samples of
training data in parallel.
Reduce correlation between different trees by taking a random sample of predictors as
candidates for each split.
Boosting (idea) - answer-In each iteration, fit a tree to the residuals of the preceding tree
and a scale-down version of current tree's predictions is added to the previous
predictions.
Each tree focuses on observations that the previous tree predicted poorly.
Backward step-wise selection - answer-start with the full model with all features and
sequentially remove features until no improvement in selected measure.
Forward Stepwise Selection - answer-start with the null model with no features and
sequentially add features until no improvement in selected measure.
BIC vs. AIC - answer-BIC is more conservative and results in simpler models.
Binarization - answer-Replacing a value with 0 or 1 based on threshold.
pros: drop individual factor levels statistically insignificant with respect to the base level.
cons: more steps in the stepAIC procedure and model may become harder to interpret.
Regularization (idea) - answer-reduce overfitting by shrinking the size of the coefficient
estimates, especially those of non-predictive features.
Reduction occurs by optimizing the log-likelihood function adjusted by a penalty term
that reflects the size of the coefficients.
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