What are the effects of missing data on machine learning models?

What are the effects of missing data on machine learning models?

Missing data can lead to biased estimates, reduced model accuracy, and can affect the generalization of the model.

How can I handle missing data in my dataset?

You can handle missing data by using techniques like imputation, removing missing values, or using algorithms that support missing data.

What are some common imputation methods?

Common imputation methods include mean/mode imputation, k-nearest neighbors, and regression imputation.

Does the amount of missing data matter?

Yes, the amount of missing data can significantly impact the model's performance. More missing data can lead to greater bias.

Are there specific models that are more robust to missing data?

Yes, some models like decision trees and random forests are generally more robust to missing data compared to linear models.