RegressionModelForecastPlotLevels

Compares and visualizes forecasted and actual values of regression models on both raw and transformed datasets.

Purpose: The RegressionModelForecastPlotLevels metric is designed to visually assess a series of regression models performance. It achieves this by contrasting the models’ forecasts with the observed data from the respective training and test datasets. The gauge of accuracy here involves determining the extent of closeness between forecasted and actual values. Accordingly, if any transformations are specified, the metric will handle transforming the data before making this comparison.

Test Mechanism: The RegressionModelForecastPlotLevels class in Python initiates with a transformation parameter, which default aggregates to None. Initially, the class checks for the presence of model objects and raises a ValueError if none are found. Each model is then processed, creating predictive forecasts for both its training and testing datasets. These forecasts are then contrasted with the actual values and plotted. In situations where a specified transformation, like “integrate,” is specified, the class navigates the transformation steps (performing cumulative sums to generate a novel series, for instance). Finally, plots are produced that compare observed and forecasted values for both the raw and transformed datasets.

Signs of High Risk: Indications of high risk or failure in the model’s performance can be derived from checking the generated plots. When the forecasted values dramatically deviate from the observed values in either the training or testing datasets, it suggests a high risk situation. A significant deviation could be a symptom of either overfitting or underfitting, both scenarios are worrying. Such discrepancies could inhibit the model’s ability to create precise, generalized results.

Strengths:

• Visual Evaluations: The metric provides a visual and comparative way of assessing multiple regression models at once. This allows easier interpretation and evaluation of their forecasting accuracy.
• Transformation Handling: This metric can handle transformations like “integrate,” enhancing its breadth and flexibility in evaluating different models.
• Detailed Perspective: By looking at the performance on both datasets (training and testing), the metric may give a detailed overview of the model.

Limitations:

• Subjectivity: Relying heavily on visual interpretations; assessments may differ from person to person.
• Limited Transformation Capability: Currently, only the “integrate” transformation is supported, implying complex transformations might go unchecked or unhandled.
• Overhead: The plotting mechanism may become computationally costly when applying to extensive datasets, increasing runtime.
• Numerical Measurement: Although visualization is instrumental, a corresponding numerical measure would further reinforce the observations. However, this metric does not provide numerical measures.