Fit()#

Description#

Fit() computes the mean and standard deviation for the specified columns from the provided table.

The computed statistics are stored internally and later used during Transform to perform Z-score standardization.

Fit() overwrites any previously stored statistics for the same columns.

Signature#

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

Parameters#

• t#

  *table.Table
  The input table used to compute column statistics.

• columns#

  ...string
  Zero or more column names to be fitted.

  Only the specified columns are processed.

  Calling Fit() with zero column names results in a no-op. In this case, IsFitted() remains returning false.

Return Value#

• error#

  Fit() returns an error if:

  • The provided table is nil
  • A specified column does not exist
  • A column contains no numeric values
  • A column has zero standard deviation

  If all columns are fitted successfully, nil is returned.

Behavior#

• Computes mean and standard deviation for each specified column
• Considers only numeric values when computing statistics
• Ignores non-numeric values during computation
• Stores computed statistics internally
• Appends fitted column names to the feature list
• Overwrites previously fitted statistics for the same columns
• Does not modify the input table

Example Usage#

After creating a ZScaler instance, call Fit() with a table and the columns to standardize.

Once Fit() succeeds, the scaler is ready to be used with Transform().

When to Use#

Use Fit() when you need to:

• Learn per-column mean and standard deviation from a reference dataset
• Prepare a ZScaler for consistent standardization
• Refit the scaler with updated or different training data

See Also#

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