Numeric Features and Scaling

Choose the entity and a reference date, keep only that entity's events inside a window ending at the reference date, then collapse them into one row: counts, sums, means, maxima, distinct counts, and days since the last event.

Z-score standardisation subtracts the mean and divides by the standard deviation, producing mean 0, standard deviation 1, and no fixed bounds. Min-max scaling rescales values into a fixed range such as 0 to 1 using the training minimum and maximum.

Equal-width bins cut the range into intervals of the same size, so a long tail crowds almost every row into the first bin and leaves near-empty bins above. Equal-frequency bins cut at quantiles, so counts are similar and widths vary.

A quotient is not a linear combination of its two columns, so no coefficients on debt and income reproduce debt divided by income. A tree can only approximate it with a staircase of axis-aligned splits, which costs depth and data.

Least squares squares residuals, so a far-out point dominates the total and rotates the line. kNN puts that huge gap into every distance, so neighbourhoods break. A tree splits on rank order, so the extreme lands in its own branch.

kNN and SVM compare examples by distance, so a feature measured in large units dominates every comparison. A tree splits one feature at a time at a threshold; rescaling preserves the ordering of values, so exactly the same splits remain available.

Plain log fails here: log(0) is undefined and negatives have no log. For a non-negative column with zeros, use log(1+x). When values drop below zero, use Yeo-Johnson, the power transform defined on the whole real line.

Ask whether B2B orders will be scored in production. If so, keep the row and represent that segment explicitly; deleting it trains a model blind to your most valuable orders. Drop only corrupt or out-of-scope rows.

-999 is a code the logger writes when the sensor is disconnected, not a measurement. Capping folds it into the real data range and teaches a relationship that never existed. Convert it to missing instead.

Window length trades freshness against stability, and the right value depends on how fast the behaviour changes. A 7-day window reacts quickly but is sparse and noisy; a 90-day window is stable but dilutes recent change. Tune it on validation data rather than guessing.

Percentile caps are parameters estimated from data. Computing them over every row lets held-out rows shape their own preprocessing, so validation scores turn optimistic. Fit the bounds on training rows and apply those stored numbers unchanged everywhere else.

Use it when a column carries extreme values. Robust scaling subtracts the median and divides by the interquartile range, statistics that a handful of extreme points barely move, so the bulk of the data still lands on a usable scale.

At 200 columns and 1,500 rows there are roughly seven rows per column, so some will look predictive by chance alone. Judge them by repeated cross-validation with the selection step refitted inside every fold, against a baseline without them.

Counts and sums are genuinely zero — nothing happened, and that is an observation. Means, maxima and shares are undefined because there is nothing to summarise, and recency has no value at all. Encode those with a sentinel plus a companion flag, never a silent zero.

Exponentiating is a convex map, so the average of log-scale predictions does not map back to the average price. Under symmetric log-scale errors it returns the conditional median; with normal log errors of standard deviation s, multiply by exp(s^2 / 2) to recover the mean.

Rarely for accuracy, often for everything else. Binning discards within-bin variation and imposes arbitrary cutpoints, so it usually costs predictive power. It earns its place when bins buy interpretability, stable reporting, or let a linear model express a non-monotone effect.

Hour 23 and hour 0 are one hour apart but 23 units apart as integers. Mapping the hour onto a circle with sin(2pih/24) and cos(2pih/24) makes them neighbours again, which matters to any distance- or magnitude-based model.

A raw count mixes how active an entity is overall with how it splits that activity and how long it was observed. Dividing by the entity's total gives composition, and dividing by days observed gives intensity, so the model can separate a heavy user from a lopsided one.

Min-max equalises each feature's range, not its spread. A heavy-tailed column whose maximum sits far above the bulk gets compressed near zero after scaling, so a well-spread bounded column ends up supplying almost all of the distance.

Rank normalisation replaces each value with its rank, rescaled to a uniform or Gaussian shape. It flattens any tail with no parametric assumption, but it keeps only the ordering: how far apart two values were is discarded.