How do I handle missing data in machine learning?

How do I handle missing data in machine learning?

Handling missing data is crucial for building effective machine learning models. You can use techniques like imputation, where you fill in missing values with estimates, or you can remove rows or columns with missing data. What specific method are you interested in?

What is imputation?

Imputation is a technique used to replace missing values with substituted values. Common methods include using the mean, median, or mode of the available data, or using more complex algorithms like k-nearest neighbors.

Are there any risks with imputation?

Yes, imputation can introduce bias if not done carefully. For instance, using the mean can distort the distribution of the data. It's important to analyze the data and choose the imputation method that best preserves its integrity.

What about using algorithms that handle missing data?

Some algorithms, like decision trees and random forests, can handle missing data natively. They can split on available features without needing imputation, which can be beneficial in preserving the original data distribution.