Sk learn 1.1 introduces an enhanced OneHotEncoder for increased efficiency.
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In the realm of data science, Scikit-learn, a popular Python library, has introduced a new feature to its OneHotEncoder tool in version 1.1. This update allows for grouping infrequent categories, a functionality that can significantly reduce computation and memory burden without losing significant value.
One-hot encoding is a common data preprocessing step that creates a column for each category in a dataset. This process is crucial as the features in a tabular dataset often need an extra step of data preprocessing before being used as input to a machine learning model.
Scikit-learn's OneHotEncoder, with its new parameter, enables users to categorise infrequent categories (those appearing fewer times than the threshold) into a single group during encoding. This grouping process can help reduce dimensionality and potential overfitting, making it a valuable addition to the data science toolkit.
To use this feature, you can configure the encoder as follows:
```python from sklearn.preprocessing import OneHotEncoder
enc = OneHotEncoder(min_frequency=6, sparse_output=False)
X_encoded = enc.fit_transform(X) ```
In this example, any category occurring fewer than 6 times will be grouped together as a single category in the one-hot encoded output.
This functionality is new as of scikit-learn version 1.7.0, simplifying the handling of rare categories without the need for separate manual preprocessing.
For those using older versions of Scikit-learn, updates can be made using pip.
Consider a sample DataFrame containing two categorical features, city and division. If a feature has 20 distinct values and 95% belong to 4 distinct values, grouping the remaining 16 distinct values into a single group can be beneficial.
It's worth noting that there is an alternative to Scikit-learn's OneHotEncoder called Feature-engine's OneHotEncoder, which allows selecting variables for transformation without the need of an extra class.
In conclusion, Scikit-learn's new OneHotEncoder feature with the parameter offers a streamlined approach to handling infrequent categories, making data preprocessing more efficient and less resource-intensive.
Data-and-cloud-computing technologies can benefit from Scikit-learn's efficient data preprocessing, as the library's OneHotEncoder tool can now group infrequent categories. This technology improvement reduces computation and memory requirements while preserving valuable data for machine learning models.
The seamless integration of the grouping functionality within Scikit-learn's OneHotEncoder makes it an essential tool for data-and-cloud-computing professionals focusing on data preprocessing in data science.
