QuantileTransformer using Scikit-learn
When is the right time to utilize QuantileTransformer?
When working with non-Gaussian distributed data or when the range of feature values is large, the QuantileTransformer is quite helpful. It may enhance machine learning model performance and lessen the effect of outliers.
What are the QuantileTransformer’s primary modes of operation?
Gaussian transformation and uniform transformation are the two primary modes available in QuantileTransformer. The data distribution is converted to a uniform distribution in uniform transformation mode and to a Gaussian distribution in Gaussian transformation mode.
QuantileTransformer using Scikit-learn
Data preprocessing is an essential step in the machine learning pipeline that involves transforming unprocessed data into a format that can be used for analysis and model construction. QuantileTransformer, a powerful module is one of the strongest tool that uses quantile information to transform features. This transformation is a useful technique for managing normal distribution and outliers.
In this article, the readers will get a thorough grasp and practical knowledge of QuantileTransformer and how we can implement it in Scikit-learn.
Table of Content
- Understanding QuantileTransformer
- Implementing QuantileTransformer with Scikit-Learn
- Quantile Transformation for Classification using Scikit-Learn
- Regression with Quantile Transformation using Scikit-Learn