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Ch. 15 Build Candidate Models (Select Target Variable - what you're…
Ch. 15 Build Candidate Models
Candidate Models: Construct of Data for Training Data
This Training Data will be used to determine which algorithm best fits the model
Select Target Variable - what you're trying to predict
focuses on delta of correct answer - counts in error
Optimizes this error
although you can customize by client how you define optimization
Methods for Data Selection: Random v Stratified Data
Random: takes random 20% for a Hold out sample
Stratified: Optimizes Distribution in holdout sample
holdout sample = fixed portion of data being held to use in real world predictions
After holdout sample, then remainder we use folds on data
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LogLos is default option for preditction
Steps for Creating Predictive Model:
Setting Target Variable (market neutral portfolio in
CQA
)
Creating CV and Holdout Partitions (~Parts - Deciles or Quintiles in
CQA
)
Characterizing Target variable (
optimizing
max return and min risk in
CQA
)
loading data set and prepping data (FNCE and utilities & factor decile choice in
CQA
)
saving target and partitioning information
Analyzing features (Backetesting results in
CQA
)
Calculating List of Models (Multiple backtests in
CQA
)
Calculating List of Models:
Sorts models by importance to HOW WELL IT PREDICTS on R^2 or linear regression (Factor correlation to risk and return in
CQA
)
Even works on Non-Linear relationships
MODEL SELECTION PROCESS
Most ML Algos reside in Amazons Cloud
The reason is because we need MULTIPLE computers (RAM) to be running these algos
TOURNAMENTS of ALGOS // MODELS (spearman correlation in
CQA
)
Algos create predictive models and these models are then placed in ranking order (least error = best)
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