Pulse Documentation

name: op-feat-sqrt
description: Per-row sqrt(x) of a numeric field; emits one f64 column.
kind: operator
category: FEAT
operator: FEAT_SQRT
type: reference
applies_to: process, compose, predict
examples_tags: [feature-engineering, distribution-shape, pre-filter, streaming-friendly]

Feature operators emit derived columns; no Response.Components.

Use when

Adds a column holding the square root of the value, a gentler squeeze than a log for counts with a moderate long tail.

Questions it answers:

• Can I soften the few very large complaint counts without a full log?
• What does the field look like with its big values pulled in a little?

Use something else:

• FEAT_LOG when the tail is very long and needs a stronger squeeze.

Params

None. Field (required, numeric) — params block is unused.

Inputs

Output

One f64 column written to Label (default SQRT_<field>). Formula sqrt(x).

Reading the output

• value: The square root of the value: 4 reads 2, 100 reads 10, 10,000 reads 100. Large values are pulled in more than small ones, but less sharply than a log.
  • Caveat: A negative value has no real square root and reads missing, with no error raised; 0 reads 0.
  • Caveat: Between 0 and 1 the root is LARGER than the value (0.25 reads 0.5), so a field of fractions is stretched, not squeezed.
  • Caveat: Averages, sums and differences of the transformed column are on the new scale: transforming them back does not give the average of the original values.

Gotchas

• Negative inputs (x < 0) emit null — sqrt of negative is undefined for real-valued features. No error.
• Null inputs propagate to null outputs.
• Gentler skew compression than a log — prefer it for moderate-skew counts.
• Streamable per-row.

See

• pulse_examples_search tags=[feature-engineering]
• Skills: feature-engineering, op-feat-log, op-feat-poly