What is Predictive Modeling?

Predictive modeling uses known results to create a model that can be used to forecast events. Although these events are usually future outcomes, predictive modeling is not restricted to this and can be applied to predict any type of unknown event.

This term is used in a broad context. In the literature, predictive modeling is usually considered synonymous with machine learning; and in the commercial environment, it is often referred to as predictive analytics.

Predictive modeling is based on statistics, regression, and machine learning. Although different, these techniques are frequently used in a combined form.

Statistical models can be classified as parametric, semi-parametric, and non-parametric. Parametric models consider assumptions about the probability distribution and are usually applied when the data is distributed normally. Non-parametric models provide no assumptions about the distribution involved and are typically used for non-Normal variables. Semi-parametric combines the other two.

Regression attempts to find relationships between independent and dependent variables. Typical methods are Ordinary Leas Squares and Ridge regression.

Machine learning is used in classification and complex regression problems. A more advanced modeling technique is deep learning, which considers neural networks with several hidden layers. Both machine learning and deep learning are part of artificial intelligence.

Why is Predictive Modeling Important?

Predictive modeling is used in many fields: from crime prevention to medicine to finance to engineering. In all cases, the question that predictive models try to answer is: what may happen under certain circumstances? For example, in finance, companies use predictive modeling to forecast customer behavior, risk, and market and economic conditions. And, in engineering, it is used to plan machine maintenance and optimize resource usage.

Predictive modeling and LogicPlum

Predictive modeling is at the heart of LogicPlum’s business. Its platform provides the unique advantage of analyzing different modeling possibilities and choosing the optimal one based on a specified metric.

Additional Resources

For those wanting to enhance their skills in predictive modeling using Python:

  • Swamynathan, M. (2019). Mastering Machine Learning with Python in Six Steps: A Practical Implementation Guide to Predictive Data Analytics Using Python