Time-Varying Models

Lingrui Gan explains how to model for parameters whose effects change over time:

We can frame conversion prediction as a binary classification problem, with outcome “1” when the visitor converts, and outcome “0” when they do not. Suppose we build a model to predict conversion using site visitor features. Some examples of relevant features are: time of day, geographical features based on a visitor’s IP address, their device type, such as “iPhone”, and features extracted from paid ads the visitor interacted with online.

A static classification model, such as logistic regression, assumes the influence of all features is stable over time, in other words, the coefficients in the model are constants. For many applications, this assumption is reasonable—we wouldn’t expect huge variations in the effect of a visitor’s device type. In other situations, we may want to allow for coefficients that change over time—as we better optimize our paid ad channel, we expect features extracted from ad interactions to be more influential in our prediction model.

Read on for more.

Related Posts

Principal Component Analysis With Faces

Mic at The Beginner Programmer shows us how to creepy PCA diagrams with human faces: PCA looks for a new the reference system to describe your data. This new reference system is designed in such a way to maximize the variance of the data across the new axis. The first principal component accounts for as […]

Read More

Using Uncertainty For Model Interpretation

Yoel Zeldes and Inbar Naor explain how uncertainty can help you understand your models better: One prominent example is that of high risk applications. Let’s say you’re building a model that helps doctors decide on the preferred treatment for patients. In this case we should not only care about the accuracy of the model, but […]

Read More

Categories

September 2017
MTWTFSS
« Aug Oct »
 123
45678910
11121314151617
18192021222324
252627282930