validmind.JarqueBera

JarqueBera

@tasks('classification', 'regression')

@tags('tabular_data', 'data_distribution', 'statistical_test', 'statsmodels')

defJarqueBera(dataset):

Assesses normality of dataset features in an ML model using the Jarque-Bera test.

Purpose

The purpose of the Jarque-Bera test as implemented in this metric is to determine if the features in the dataset of a given Machine Learning model follow a normal distribution. This is crucial for understanding the distribution and behavior of the model's features, as numerous statistical methods assume normal distribution of the data.

Test Mechanism

The test mechanism involves computing the Jarque-Bera statistic, p-value, skew, and kurtosis for each feature in the dataset. It utilizes the 'jarque_bera' function from the 'statsmodels' library in Python, storing the results in a dictionary. The test evaluates the skewness and kurtosis to ascertain whether the dataset follows a normal distribution. A significant p-value (typically less than 0.05) implies that the data does not possess normal distribution.

Signs of High Risk

  • A high Jarque-Bera statistic and a low p-value (usually less than 0.05) indicate high-risk conditions.
  • Such results suggest the data significantly deviates from a normal distribution. If a machine learning model expects feature data to be normally distributed, these findings imply that it may not function as intended.

Strengths

  • Provides insights into the shape of the data distribution, helping determine whether a given set of data follows a normal distribution.
  • Particularly useful for risk assessment for models that assume a normal distribution of data.
  • By measuring skewness and kurtosis, it provides additional insights into the nature and magnitude of a distribution's deviation.

Limitations

  • Only checks for normality in the data distribution. It cannot provide insights into other types of distributions.
  • Datasets that aren't normally distributed but follow some other distribution might lead to inaccurate risk assessments.
  • Highly sensitive to large sample sizes, often rejecting the null hypothesis (that data is normally distributed) even for minor deviations in larger datasets.