Advantages of PowerTransformer
- Handling Skewed Data: Many real-world datasets exhibit skewness, where the distribution of values is asymmetric. PowerTransformer can effectively mitigate this skewness, making the data distribution more symmetrical, which can benefit the performance of certain machine learning algorithms.
- Preservation of Rank Order: Unlike some other transformations, such as min-max scaling, PowerTransformer preserves the rank order of the data. This is important when the relative ordering of values carries meaningful information, as is often the case in many applications.
- Robustness to Outliers: PowerTransformer is relatively robust to outliers compared to some other transformations. Outliers can significantly impact the performance of models, and the ability to handle them effectively is a valuable asset.
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