What is a PowerTransformer?
The PowerTransformer is a technique used to make numerical data resemble a Gaussian distribution more closely, which is often required for many machine learning models that operate under the assumption of normal distribution. It is especially valuable in situations where data shows significant skewness or kurtosis. By stabilizing variance and reducing skewness, the PowerTransformer helps to reinforce the foundational statistical assumptions, thus enhancing the effectiveness of the model.
PowerTransformer in scikit-learn
When it comes to data preprocessing, machine learning algorithms perform better when variables are transformed to fit a more Gaussian distribution. PowerTransformer is a scikit-learn library that is used to transform to fit Gaussian distribution. The article aims to explore PowerTransfoer technique, its methods along with implementation in scikit-learn.
Table of Content
- What is a PowerTransformer?
- How Does PowerTransformer Work?
- Box-Cox Transform
- Yeo-Johnson Transform
- Implementation: PowerTransformer in Scikit-Learn
- Step 1: Import Libraries
- Step 2: Generating Skewed Data
- Step 3: Applying PowerTransformer
- Advantages of PowerTransformer