How Quantile Transformer Works for Outlier Detection?

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....

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....

Quantile Transformation Approaches for Outlier Identification

The quantile transformer transforms features using quantile information. It is applied to each feature independently. The steps are as follows:...

How Quantile Transformer Works for Outlier Detection?

The quantile transformer uses the quantile function to rank the relationship between each observation. Here, the quantile function may follow a normal or uniform distribution. The function is applied to each feature where the transformer spreads the most frequent values, thereby reducing the impact of outliers. Here, it doesn’t remove the outlier but shrinks them to a defined range, thereby making them indistinguishable from inliers....

Utilizing Quantile Transformer for Outlier Detection in Scikit-learn

Scikit-Learn provides a handy class to take care of data transformation using quantile functions. The details are as follows:...

Advantages and Disadvantages of Quantile Transformer for Outlier Detection

Let’s look at the advantages and limitations of using a quantile transformer for outliers....

Conclusion

Most of the machine learning algorithms perform well with a uniform or normal data distribution. A quantile transformer is a useful tool that automatically transforms a dataset into a uniform or normal data distribution. Here, the entire data (including the outliers) is mapped to a uniform distribution, which makes the outliers indistinguishable from the inliers....