Data can change over time. This can result in poor and degrading predictive performance in predictive models that assume a static relationship between input and output variables.
This problem of the changing underlying relationships in the data is called concept drift in the field of machine learning. In this post, you will discover the problem of concept drift and ways to you may be able to address it in your own predictive modeling problems.
After completing this post, you will know:
This post is divided into 3 parts; they are:
Predictive modeling is the problem of learning a model from historical data and using the model to make predictions on new data where we do not know the answer.
Technically, predictive modeling is the problem of approximating a mapping function (f) given input data (X) to predict an output value (y). Often, this mapping is assumed to be static, meaning that the mapping learned from historical data is just as valid in the future on new data and that the relationships between input and output data do not change.
This is true for many problems, but not all problems.
In some cases, the relationships between input and output data can change over time, meaning that in turn there are changes to the unknown underlying mapping function. The changes may be consequential, such as that the predictions made by a model trained on older historical data are no longer correct or as correct as they could be if the model was trained on more recent historical data. Read more from machinelearningmastery.com…
thumbnail courtesy of machinelearningmastery.com