SOA/ASA/PA 기출 및 내용정리 - 21.12.13시험(기록용)

Task6

(a) Explain what the variance and bias values indicate about the relative quality of predictions when comparing predictive models.

 The variance figures indicate how much the predictions vary depending on the training data used. As more predictors are used, the variance increases because the model more predicsely fits the training data for each trial and becomes less generalized. The bias figures indicate how close expected predictions and actual results are on unseen data. Generally, as more predictors are used, the bias decreases as more accurate predictions are made.

 

 

Task7

(a) Explain, for a general audience, what cost complexity pruining does.

 Cost complexity pruninig is part of a two-step approach to building a tree model. The first step is to build a large, complex decision tree, which is essentially a flow chart for deciding whether to try to transfer an animal.

 A second step called pruning is taken. Pruning reduces the size and complexity of the initial flow chart to a more useful one. That is is called cost complexity pruning has to do with the technical tradeoff being made between how simple the flow chart is compared to how well it distinguishes whether animals can be transferred or not.

 

 

Task8

(a) Boosting - Setting eta as high as possible?

 In boosting algorithms, which work by iteratively fitting a model the residuals of a prior learner, eta, also called the learning rate or shrinkage parameter, slows down the model fitting process so that the residuals from the prior learner do not have too large an influence on the final model. With eta at its maximum of 1, each model iteration is the prior learner plus the model fitting its residuals. While this will run quickly, it will be prone to high variance, overfitting the training data and not generalizing well to unseen data. Setting eta to less than 1 slows down the fitting process by only adding eta times the model fitting the residuals to form the next learner and will substantially reduce the variance.

 

(b) Explain cross validation and how it can be used to set the eta hyperparameter

 Cross validation divides the availabel data in to multiple folds for a series of model fitting runs. Each fold is used as test data exactly once. The average test metric across the runs is the result of the cross validation.

 To use cross validation to set the eta hyperparameter, a series of reasonable values for eta would be chosen beforehand. Then, for each value of eta, cross validation would be performed with each model fitting run using the same eta. The result is one average test metric result from each cross validation for each value of eta. The value of eta with the superior test metric, some measure of predictive power on unseen data, would be shosen for subsequent predictive modeling work.