Gartner Blog Network


Prediction Models: Traditional versus Machine Learning

by Jitendra Subramanyam  |  June 8, 2019  |  Submit a Comment

Meet the Chief Data and Analytics Officer research team | Check out our research

Machine learning models are constructed differently from traditional quantitative models.

Two Types of Traditional Prediction Models

In the first type of traditional prediction model, the input data set along with statistical assumptions and calculations determine the prediction algorithm. The input data set is analyzed (or “fitted to the data”) using statistical techniques. The prediction algorithm that is the one best suited to describing the data as determined by the statistical analysis.

Traditional Prediction Model

Traditional Prediction Model – Fitting Model to Data

The second type of traditional prediction model uses an explicit set of rules (e.g., if X then Y) to transform the inputs into a prediction. Instead of the prediction algorithm being “discovered” through statistical calculations, these rules are usually ones that are known by experts in the prediction domain (e.g., the medical knowledge physicians have in diagnosing/predicting a disease).

Prediction Based on Rules

Prediction Based on Rules

Machine Learning Models

In contrast to the traditional quant prediction models, machine learning prediction models are developed in two steps.

In the first step, a machine learning model is trained. In this training step the input data, the historical results associated with these inputs, and a training algorithm are used to iteratively arrive at the prediction algorithm.

Machine Learning Model: Training Step

Machine Learning Model: Training Step

At the end of the first step, the model is “trained” and is now ready to make predictions.

In the second step, the prediction step, the trained machine learning model uses the prediction algorithm arrived at in the training step to transform new inputs into predictions.

Trained Machine Learning Model

Trained Machine Learning Model

You may have noticed that I’ve used “model”, and “algorithm”. Is there a difference? This gets us to some terminology that’s useful to have in mind when you read about machine learning.

model is a mathematical expression that transforms inputs into outputs. The model by itself cannot be used to calculate a result. To do so requires fixing the model’s parameter values. In traditional approaches, the parameter values are fixed based on statistical calculations. In machine learning the parameter values are fixed in the process of training the model. Without its parameter values specified, a model is a structure, a shell, a high-level directive for transforming inputs into outputs. With the parameter values specified, the model can be used to generate specific predictions.

An algorithm is a recipe — a clearly defined step by step by step process — for turning a set of inputs into an output.

prediction algorithm is the specific mathematical expression that results once the parameter values of the model are fixed. In machine learning these parameter values are fixed during the iterative training process which itself proceeds by using an algorithm. The most common of these training algorithms is called gradient descent.

Additional Resources

Category: artificial-intelligence  data-and-analytics-leaders  

Tags: machine-learning  prediction  

I work on practical case studies templates and tools that serve Data and Analytics teams worldwide.






Leave a Reply

Your email address will not be published. Required fields are marked *

Comments or opinions expressed on this blog are those of the individual contributors only, and do not necessarily represent the views of Gartner, Inc. or its management. Readers may copy and redistribute blog postings on other blogs, or otherwise for private, non-commercial or journalistic purposes, with attribution to Gartner. This content may not be used for any other purposes in any other formats or media. The content on this blog is provided on an "as-is" basis. Gartner shall not be liable for any damages whatsoever arising out of the content or use of this blog.