LJungBox

Assesses autocorrelations in dataset features by performing a Ljung-Box test on each feature.

Purpose: The Ljung-Box test is a type of statistical test utilized to ascertain whether there are autocorrelations within a given dataset that differ significantly from zero. In the context of a machine learning model, this test is primarily used to evaluate data utilized in regression tasks, especially those involving time series and forecasting.

Test Mechanism: The test operates by iterating over each feature within the training dataset and applying the acorr_ljungbox function from the statsmodels.stats.diagnostic library. This function calculates the Ljung-Box statistic and p-value for each feature. These results are then stored in a dictionary where the keys are the feature names and the values are dictionaries containing the statistic and p-value respectively. Generally, a lower p-value indicates a higher likelihood of significant autocorrelations within the feature.

Signs of High Risk: - A high risk or failure in the model’s performance relating to this test might be indicated by high Ljung-Box statistic values or low p-values. - These outcomes suggest the presence of significant autocorrelations in the respective features. If not properly consider or handle in the machine learning model, these can negatively affect model performance or bias.

Strengths: - The Ljung-Box test is a powerful tool for detecting autocorrelations within datasets, especially in time series data. - It provides quantitative measures (statistic and p-value) that allow for precise evaluation of autocorrelation. - This test can be instrumental in avoiding issues related to autoregressive residuals and other challenges in regression models.

Limitations: - The Ljung-Box test cannot detect all types of non-linearity or complex interrelationships among variables. - Testing individual features may not fully encapsulate the dynamics of the data if features interact with each other. - It is designed more for traditional statistical models and may not be fully compatible with certain types of complex machine learning models.