Understanding Quantile Transformer
The QuantileTransformer in Scikit-Learn is a powerful tool for transforming features in a dataset to follow a specific distribution, such as a Gaussian or Uniform distribution. This transformation is particularly useful in machine learning when the assumption of normality is required for certain models or when the data is highly skewed.
The QuantileTransformer works by mapping the quantiles of the input data to the quantiles of the desired output distribution. This is done without assuming knowledge of the true distribution of the data. Instead, it relies on the empirical distribution of the data, which is the distribution of the observed data points.
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