Practical insights

Results

8 results (published only)

• Feature engineering and preprocessing

  Use RobustScaler when a few extreme values distort mean-and-variance scaling, then decide about the extremes separately

  A scaled feature has a handful of huge values, such as a 10x passenger outlier or a corporate group booking.

  easydocumented fact

• Feature engineering and preprocessing

  Do not give a linear model ordinal codes for unordered categories

  A nominal feature such as market or payment method is label-encoded as 0, 1, 2 and fed to a linear, kernel or distance model.

  easypractitioner heuristic

• Feature engineering and preprocessing

  Replace sentinel values like -999 before they reach linear or distance models

  A source system encodes missing or not-applicable as -999, 9999 or 0.

  easypractitioner heuristic

• Feature engineering and preprocessing

  Guard ratio features against zero and tiny denominators

  You build ratios such as revenue per seat, cancellations per booking or spend per visit.

  easypractitioner heuristic

• Feature engineering and preprocessing

  Log-transform skewed inputs for linear and distance models, not for trees

  A numeric feature spans orders of magnitude, such as corporate account spend or group size.

  easypractitioner heuristic

• Feature engineering and preprocessing

  Remove features that are recorded because of the outcome, not before it

  A candidate feature is filled in by a process that happens after or because of the target event, such as a cancellation reason, refund amount or collections flag.

  easydocumented fact

• Feature engineering and preprocessing

  Check the realised bin count when quantile-binning a zero-inflated feature

  KBinsDiscretizer with strategy quantile and ten bins is applied to a feature with a large spike, for example 60% zeros in days since last cancellation.

  easydocumented fact

• Feature engineering and preprocessing

  Compute distances from coordinates with a great-circle formula in radians, not Euclidean on degrees

  A model needs distance between origins and destinations, or a nearest-station feature, from latitude and longitude columns, and the step runs on raw degree values.

  easypractitioner heuristic