The target variable of typical regression datasets is somewhat evenly distributed.

But, at times, the target variable may have plenty of zeros. Such datasets are called zero-inflated datasets.

They may raise many problems during regression modeling. This is because a regression model can not always predict exact “zero” values when, ideally, it should.

For instance, consider simple linear regression. The regression line will output exactly “zero” only once (if it has a non-zero slope).

This issue persists:

Not only in higher dimensions...

But also in complex models like neural nets for regression.

One great way to solve this is by training a combination of a classification and a regression model.

This goes as follows:

Mark all non-zero targets as “1” and the rest as “0”.

Train a binary classifier on this dataset.

Next, train a regression model only on those data points with a

**non-zero true target**.

During prediction:

If the classifier's output is “0”, the final output is also zero.

If the classifier's output is “1”, use the regression model to predict the final output.

Its effectiveness over the regular regression model is evident from the image below:

Linear regression alone underfits the data.

Linear regression with a classifier performs as expected.

👉 Over to you: What are other ways to train a model on a zero-inflated dataset?

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## How Zero-inflated Datasets Ruin Your Regression Modeling

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