# LogitScaler **Compatibility:** `numerical` data The `LogitScaler` performs a statistical transformation on numerical data. It computes the [Logit function](https://en.wikipedia.org/wiki/Logit) to convert the scale and shape of the data. You can optionally choose the min and max values of the Logit function, effectively enforcing the min/max boundaries on your data.

```python from rdt.transformers.numerical import LogitScaler transformer = LogitScaler(min_value=0, max_value=100) ``` ## Parameters **`missing_value_replacement`**: Add this argument to replace missing values during the transform phase


(default) `'mean'`	Replace all missing values with the average value.
`'random'`	Replace missing values with a random value. The value is chosen uniformly at random from the min/max range.
`'mode'`	Replace all missing values with the most frequently occurring value
`<number>`	Replace all missing values with the specified number (`0`, `-1`, `0.5`, etc.)
`None`	Do not replace missing values. The transformed data will continue to have missing values.

**`missing_value_generation`**: Add this argument to determine how to recreate missing values during the reverse transform phase


(default) `'random'`	Randomly assign missing values in roughly the same proportion as the original data.
`'from_column'`	Create a new column to store whether the value should be missing. Use it to recreate missing values. Note: Adding extra columns uses more memory and increases the RDT processing time.
`None`	Do not recreate missing values.

**`min_value`**: The min value for the Logit function. The Logit function of anything ≤ this value is undefined.


(default) `0`	The min value of the function is 0.
`<float>`	A floating point value that acts as as the min value of the Logit function.

**`max_value`**: The max value for the Logit function. The Logit function of anything ≥ this value is undefined.


(default) `1`	The max value of the function is 1.
`<float>`	A floating point value that acts as as the max value of the Logit function.

**`learn_rounding_scheme`**: Add this argument to allow the transformer to learn about rounded values in your dataset.


(default) `False`	Do not learn or enforce any rounding scheme. When reverse transforming the data, there may be many decimal places present.
`True`	Learn the rounding rules from the input data. When reverse transforming the data, round the number of digits to match the original.

## FAQ

How do I enforce min/max values with this transformer?

This transformer enforces strict minimum and maximum boundaries on your data using the `min_value` and `max_value` parameters. If you'd like the minimum or maximum value to be allowed, we suggest setting the boundary to something slightly outside the range that you need. ```python from rdt.transformers.numerical import LogitScaler transformer = LogitScaler( min_value=0.0 - 1e-10, # allow a value of exactly 0 max_value=10.0 + 1e-10 # allow a value of exactly 10 ) ``` If you'd like to enforce singular boundary (min or max) instead, please use the [LogScaler](https://docs.sdv.dev/rdt/transformers-glossary/numerical/logscaler).