Transform()#

Description#

Transform() applies Z-score standardization to the specified columns of a table using statistics computed during Fit.

It returns a new table with standardized values while leaving the original table unchanged.
Only numeric values are transformed; non-numeric values are preserved as-is.

Standardization is performed using the mean and standard deviation learned during the Fit phase.

Signature#

func (s *ZScaler) Transform(t *table.Table, columns ...string) (*table.Table, error)

Parameters#

• t#

  *table.Table
  The input table to be transformed.

• columns#

  ...string
  One or more column names to be standardized.

If no column names are provided, Transform() uses the columns previously fitted by Fit().

Return Value#

• *table.Table#

  A new table containing the transformed data.

• error#

  Transform() returns an error if:

  • The provided table is nil
  • A specified column does not exist
  • No columns are specified and the scaler has no fitted features
  • A column was not fitted
  • A column has zero standard deviation

If the transformation succeeds, the transformed table and nil error are returned.

Behavior#

• Creates a clone of the input table before applying transformations
• Standardizes numeric values using the formula:

  (x - mean) / std

• Preserves non-numeric values without modification
• Applies standardization only to the specified columns
• Uses fitted columns by default when no columns are explicitly provided
• Returns an error if a column was not fitted
• Returns an error if a column has zero standard deviation
• Does not modify the original table

Example Usage#

After fitting a ZScaler, call Transform() to apply standardization to the desired columns.

Transform() can be called multiple times using the same fitted statistics.

When to Use#

Use Transform() when you need to:

• Standardize numeric features to zero mean and unit variance
• Apply consistent normalization to new or existing data
• Prepare data for statistical or machine learning algorithms
• Perform non-mutating transformations in data processing pipelines

See Also#

• (*ZScaler).Fit(t *table.Table, columns ...string)
• (*ZScaler).Mean()
• (*ZScaler).Std()
• (*ZScaler).IsFitted()
• (*ZScaler).Reset()