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The Evolution of Analytics
book

The Evolution of Analytics

by Patrick Hall, Wen Phan, Katie Whitson
May 2016
Intermediate to advanced content levelIntermediate to advanced
12 pages
56m
English
O'Reilly Media, Inc.
Content preview from 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 ...

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Publisher Resources

ISBN: 9781492042570Errata Page