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What are some effective techniques for handling missing data during feature engineering in machine learning?
Asked on Dec 07, 2025
Answer
Handling missing data is crucial in feature engineering for machine learning to ensure model accuracy and reliability. Here are some effective techniques you can use:
Example Concept: Common techniques for handling missing data include imputation, deletion, and using algorithms that support missing values. Imputation involves filling in missing values with mean, median, or mode for numerical data, or using a constant or the most frequent value for categorical data. Deletion can be applied when the amount of missing data is small, either by removing rows with missing values or entire columns if they have too many missing entries. Some algorithms, like decision trees, can inherently handle missing values by using surrogate splits.
Additional Comment:
- Imputation is often preferred when data loss from deletion might impact model performance.
- Advanced imputation techniques include using machine learning models to predict missing values based on other features.
- Consider the impact of missing data on your model and choose a technique that aligns with your data distribution and model requirements.
- Always evaluate the effect of your chosen method on model performance using cross-validation.
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