Numeric Scoring Metrics: Find the Right Metric for a Prediction Model

Numeric Scoring Metrics: Find the Right Metric for a Prediction Model

In this article, we will describe five real-world use cases of numeric prediction models, and in each use case, we measure the prediction accuracy from a slightly different point of view.

Quantitative data have endless stories to tell!

Daily closing prices tell us about the dynamics of the stock market, small smart meters about the energy consumption of households, smartwatches about what’s going on in the human body during an exercise, and surveys about some people’s self-estimation of a topic at some point in time. Different types of experts can tell these stories: financial analysts, data scientists, sports scientists, sociologists, psychologists and so on. Their stories are based on models, for example, regression modelstime series models and ANOVA models.

Why Are Numeric Scoring Metrics Needed?

These models have many consequences in the real world, from the decisions of the portfolio managers to the pricing of electricity at different times of the day, week and year. Numeric scoring metrics are needed in order to:

  • Select the most accurate model
  • Estimate the real-world impact of the error of the model

In this article, we will describe five real-world use cases of numeric prediction models, and in each use case, we measure the prediction accuracy from a slightly different point of view. In one case, we measure if a model has a systematic bias, and in another, we measure a model’s explanation power. The article concludes with a review of the numeric scoring metrics, showing the formulas to calculate them, and a summary of their properties. We’ll also link to a few example implementations of building and evaluating a prediction model in KNIME Analytics Platform.

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