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 models, time series models and ANOVA models.
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:
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.
Most popular Data Science and Machine Learning courses — August 2020. This list was last updated in August 2020 — and will be updated regularly so as to keep it relevant
Learning is a new fun in the field of Machine Learning and Data Science. In this article, we’ll be discussing 15 machine learning and data science projects.
This post will help you in finding different websites where you can easily get free Datasets to practice and develop projects in Data Science and Machine Learning.
The agenda of the talk included an introduction to 3D data, its applications and case studies, 3D data alignment and more.
Data Science and Analytics market evolves to adapt to the constantly changing economic and business environments. Our latest survey report suggests that as the overall Data Science and Analytics market evolves to adapt to the constantly changing economic and business environments, data scientists and AI practitioners should be aware of the skills and tools that the broader community is working on. A good grip in these skills will further help data science enthusiasts to get the best jobs that various industries in their data science functions are offering.