The Evolution of Analytics

Appendix A. Appendix A. Machine Learning Quick Reference: Best Practices

Topic	Common Challenges	Suggested Best Practice
Data Preparation
Data collection	Biased data Incomplete data The curse of dimensionality Sparsity	Take time to understand the business problem and its context Enrich the data Dimension-reduction techniques Change representation of data (e.g., COO)
“Untidy” data	Value ranges as columns Multiple variables in the same column Variables in both rows and columns	Restructure the data to be “tidy” by using the melt and cast process
Outliers	Out-of-range numeric values and unknown categorical values in score data Undue influence on squared loss functions (e.g. regression, GBM, and k-means)	Robust methods (e.g. Huber loss function) Discretization (binning) Winsorizing
Sparse target variables	Low primary event occurrence rate Overwhelming preponderance of zero or missing values in target	Proportional oversampling Inverse prior probabilities Mixture models
Variables of disparate magnitudes	Misleading variable importance Distance measure imbalance Gradient dominance	Standardization
High-cardinality variables	Overfitting Unknown categorical values in holdout data	Discretization (binning) Weight of evidence Leave-one-out event rate
Missing data	Information loss Bias	Discretization (binning) Imputation Tree-based modeling techniques
Strong multicollinearity	Unstable parameter estimates ...