How to Handle Missing Data in Logistic Regression?
Handling missing data in logistic regression is important to ensure the accuracy of the model. Some of the strategies for handling mission data are discussed below:
- Remove missing data
- Imputation: Imputation involves replacing missing values with estimated values. Common imputation techniques include:
- Mean or median imputation
- Mode imputation
- Predictive imputation
- Create a missingness indicator
How to Handle Missing Data in Logistic Regression?
Logistic regression is a robust statistical method employed to model the likelihood of binary results. Nevertheless, real-world datasets frequently have missing values, presenting obstacles while fitting logistic regression models. Dealing with missing data effectively is essential to prevent skewed estimates and maintain the model’s accuracy. In this article, we have discussed how can we handle missing data in logistic regression.
Table of Content
- How to Handle Missing Data in Logistic Regression?
- 1. Handling Missing Data in Logistic Regression by Deletion
- 2. Handling Missing Data in Logistic Regression by Imputation
- 3. Handling Missing Data in Logistic Regression using Missingness Indicator