Quantile Transformer for Detecting Outliers
In the context of outlier detection, the QuantileTransformer can be used to transform the data in a way that makes outliers more visible. By transforming the data to a Uniform distribution, outliers will be mapped to the extremes of the distribution, making them more distinguishable from inliers. It can efficiently reduce the impact of outliers, and therefore it is a robust preprocessing scheme.
- By dividing the data to n number of quantiles and scaling them makes the quantile transformer less sensitive to outliers.
- Qunatiles are efficient for comparing the distribution of datasets. By comparing different quantile values, one can gain insights about the spread and central tendency.
- It is efficient for dealing with non-Gaussian distributed data or data with large features.
Quantile Transformer for Outlier Detection
Data transformation is a mathematical function that changes the data into a scaled value, which makes it possible to compare different columns, e.g., salary in INR with weight in kilograms. Transforming the data will satisfy certain mathematical assumptions such as normalization, standardization, homogeneity, linearity, etc. Quantile Transformer is one of the data transformer techniques for standardizing data.
In this article, we will dig deep into the Quantile Transformer and will understand and implement the significance of quantile transformer for detecting outlier.
Table of Content
- Understanding Quantile Transformer
- Quantile Transformer for Detecting Outliers
- Quantile Transformation Approaches for Outlier Identification
- 1. Uniform Distribution
- 2. Normal Distribution (Gaussian)
- How Quantile Transformer Works for Outlier Detection?
- Utilizing Quantile Transformer for Outlier Detection in Scikit-learn
- Advantages and Disadvantages of Quantile Transformer for Outlier Detection