validmind.RegressionModelForecastPlot

RegressionModelForecastPlot

@tags('time_series_data', 'forecasting', 'visualization')

@tasks('regression')

defRegressionModelForecastPlot(model:validmind.vm_models.VMModel,dataset:validmind.vm_models.VMDataset,start_date:Union[str, None]=None,end_date:Union[str, None]=None):

Generates plots to visually compare the forecasted outcomes of a regression model against actual observed values over a specified date range.

Purpose

This metric is useful for time-series models or any model where the outcome changes over time, allowing direct comparison of predicted vs actual values. It can help identify overfitting or underfitting situations as well as general model performance.

Test Mechanism

This test generates a plot with the x-axis representing the date ranging from the specified "start_date" to the "end_date", while the y-axis shows the value of the outcome variable. Two lines are plotted: one representing the forecasted values and the other representing the observed values. The "start_date" and "end_date" can be parameters of this test; if these parameters are not provided, they are set to the minimum and maximum date available in the dataset.

Signs of High Risk

  • High risk or failure signs could be deduced visually from the plots if the forecasted line significantly deviates from the observed line, indicating the model's predicted values are not matching actual outcomes.
  • A model that struggles to handle the edge conditions like maximum and minimum data points could also be considered a sign of risk.

Strengths

  • Visualization: The plot provides an intuitive and clear illustration of how well the forecast matches the actual values, making it straightforward even for non-technical stakeholders to interpret.
  • Flexibility: It allows comparison for multiple models and for specified time periods.
  • Model Evaluation: It can be useful in identifying overfitting or underfitting situations, as these will manifest as discrepancies between the forecasted and observed values.

Limitations

  • Interpretation Bias: Interpretation of the plot is subjective and can lead to different conclusions by different evaluators.
  • Lack of Precision: Visual representation might not provide precise values of the deviation.
  • Inapplicability: Limited to cases where the order of data points (time-series) matters, it might not be of much use in problems that are not related to time series prediction.