Interpret model, examine impt variables in each method - See what explains the variation in y

INSTRUCTIONS TO CANDIDATES

ANSWER ALL QUESTIONS

1. Prepare/explore sample data

See the variables, the spread, SD, mean

Outliers, unrealistic values, NA values --> remove .

2. Divide into training and test set

3. Use training set

a. Choose kfold cv to choose the best model of each method

i. Eg. Regularised regression - best lambda and alpha

ii. Support vector Machines - best model with hyperparameters

iii. Random forest - parameters

b. Select overall best method - using performance of cv folds

4. Use test set

a. Depicts predictive performance of best models of each method

b. Predictive accuracy

5. Using training set

a. Interpret model, examine impt variables in each method - See what explains the variation in y

b. Examine effects of impt predictors

c. Diff methods may have diff impt variables

i. Eg. Simple models – regression and elastic net show variables 1 and 2 are impt, rsg = .25

ii. But random forest and support vector machines show 1 2 3 4 are impt, rsq = .75

iii. Means maybe have interaction or non linear reasons. Specify in simple models and see if it increases Rsq. Develop model and refine it

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