# UniformEncoder
**Compatibility:** `categorical` or `boolean` data
The `UniformEncoder` transforms data that represents categorical values into a uniform distribution in the `[0,1]` interval. It is highly accurate at preserving the overall frequencies of each category.
```python
from rdt.transformers.categorical import UniformEncoder
transformer = UniformEncoder()
```
## Parameters
**`order_by`**: Apply a prescribed ordering scheme. Use this if the discrete categorical values have an order.
(default) None
Do not apply a particular order
'numerical_value'
If the data is represented by integers or floats, order by those values
'alphabetical'
If the data is represented by strings, order them alphabetically.
### Examples
```python
from rdt.transformers.categorical import UniformEncoder
transformer = UniformEncoder(
order_by='alphabetical'
)
```
The transformer assigns each category to a unique, non-overlapping subset of the `[0,1]` interval. The length of the interval is based on the category's frequency. For example if category `'CASH'` occurs with 60% frequency, the subset will have the length `0.6` such as `[0.2, 0.8]`.
## Attributes
After fitting the transformer, you can access the learned values through the attributes.
**`frequencies`**: A dictionary that maps each category value to the observed frequency, as a float between 0 and 1
```python
>>> transformer.frequencies
{
'CREDIT': 0.2,
'CASH': 0.6,
'DEBIT': 0.2
}
```
**`intervals`**: A dictionary that maps each category value to an interval between `[0,1]`. This allows you to determine the exact rules used for transforming and reverse transforming.
```python
>>> transformer.intervals
{
'CREDIT': [0, 0.2],
'CASH': [0.2, 0.8],
'DEBIT': [0.8, 1.0]
}
```
## FAQs
When should I use this transformer?
The UniformEncoder is shown to preserve the frequency of each category value with high accuracy. This is especially useful if you have a data imbalance, for example if `True` occurs only 1% of the time while `False` occurs 99% of the time.
When should I use the order_by parameter?
Use this parameter when the categorical data is ordinal (has a specific order) and the order can easily be discovered through sorting. For example, you might storing survey responses as `'response_00'`, `'response_01'`, `'response_02'`, etc.
Don't add this parameter if it isn't necessary. Ordering increases the time it takes for transformation.
What if I'd like to sort the values by a custom order?
In some cases, your categories may not have an alphanumeric ordering scheme. Use the [**OrderedUniformEncoder**](https://docs.sdv.dev/rdt/transformers-glossary/categorical/ordereduniformencoder) to add your own, custom sorting order.
What happens to missing values?
This transformer treats missing values as if they are a new category of data.
