What is a zero-inflated model?
In statistics, a zero-inflated model is a statistical model based on a zero-inflated probability distribution, i.e. a distribution that allows for frequent zero-valued observations. One well-known zero-inflated model is Diane Lambert ‘s zero-inflated Poisson model, which concerns a random event containing excess zero-count data in unit time.
How do you train a regression with a non-zero output?
Train a classifier C that tells us whether the regression output is zero, or not. Train a regressor R on the part of the data with a non-zero target. If a data point goes into the model, the classifier first checks if the output should be zero. If yes, output zero. Otherwise, output the result of the regressor for this data point.
What is a zero inflated Poisson regression model?
Specifically, we’ll focus on the Zero Inflated Poisson regression model, often referred to as the ZIP model. Let’s briefly look at the structure of a regular Poisson model before we see how its structure is modified to handle excess zero counts. Imagine a data set containing n samples and p regression variables per sample.
Can zero-inflated data be a target for regression?
We have looked at datasets for regression that have a large number of zeroes as targets — zero-inflated data. This can unsettle many regressors such as support vector machines as well as neural networks.