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ML.PREPROCESSING.MIN_MAX_SCALER

Scales each feature to a given range, usually between 0 and 1.

Syntax

ML.PREPROCESSING.MIN_MAX_SCALER()

Returns

A MinMaxScaler transformer handle, ready to pass into ML.FIT_TRANSFORM or ML.PIPELINE.

When to use

Use min_max_scaler when a downstream model or visualization needs values strictly inside [0, 1]. Each feature is rescaled so the smallest value seen during fit becomes 0 and the largest becomes 1, with everything else linearly between.

Compared to the alternatives in this namespace:

  • Use ML.PREPROCESSING.STANDARD_SCALER as the default for most ML workflows — it handles algorithms that assume zero-centered features.
  • Use min_max_scaler when you need bounded outputs (e.g. for image pixel-style features or models that explicitly expect [0, 1] inputs).
  • Use ML.PREPROCESSING.ROBUST_SCALER when your data has outliers that would stretch the min-max range.

Examples

Rescale each column of A2:E100 to the [0, 1] range:

=ML.PREPROCESSING.MIN_MAX_SCALER()
=ML.FIT_TRANSFORM(H1, A2:E100)

Wrap the scaler and a model in a pipeline so the same scaling is applied at predict time:

=ML.PREPROCESSING.MIN_MAX_SCALER()
=ML.CLASSIFICATION.LOGISTIC()
=ML.PIPELINE(H1, H2)
=ML.FIT(H3, A2:E100, F2:F100)
=ML.PREDICT(H4, A101:E110)

Remarks

  • Outliers in the training data set the bounds. A single extreme row makes every other value cluster near 0 or 1 — switch to ML.PREPROCESSING.ROBUST_SCALER if outliers are common.
  • Always fit on the training data only, then ML.TRANSFORM the test data through the same fitted scaler. Test rows outside the original min/max will simply fall outside [0, 1] — that is expected behavior.

See also