validmind.Skewness

Skewness

@tags('data_quality', 'tabular_data')

@tasks('classification', 'regression')

defSkewness(dataset,max_threshold=1):

Evaluates the skewness of numerical data in a dataset to check against a defined threshold, aiming to ensure data quality and optimize model performance.

Purpose

The purpose of the Skewness test is to measure the asymmetry in the distribution of data within a predictive machine learning model. Specifically, it evaluates the divergence of said distribution from a normal distribution. Understanding the level of skewness helps identify data quality issues, which are crucial for optimizing the performance of traditional machine learning models in both classification and regression settings.

Test Mechanism

This test calculates the skewness of numerical columns in the dataset, focusing specifically on numerical data types. The calculated skewness value is then compared against a predetermined maximum threshold, which is set by default to 1. If the skewness value is less than this maximum threshold, the test passes; otherwise, it fails. The test results, along with the skewness values and column names, are then recorded for further analysis.

Signs of High Risk

  • Substantial skewness levels that significantly exceed the maximum threshold.
  • Persistent skewness in the data, indicating potential issues with the foundational assumptions of the machine learning model.
  • Subpar model performance, erroneous predictions, or biased inferences due to skewed data distributions.

Strengths

  • Fast and efficient identification of unequal data distributions within a machine learning model.
  • Adjustable maximum threshold parameter, allowing for customization based on user needs.
  • Provides a clear quantitative measure to mitigate model risks related to data skewness.

Limitations

  • Only evaluates numeric columns, potentially missing skewness or bias in non-numeric data.
  • Assumes that data should follow a normal distribution, which may not always be applicable to real-world data.
  • Subjective threshold for risk grading, requiring expert input and recurrent iterations for refinement.