3.1.1. Handling Missing Values and Outliers

First Principle: Effectively handling missing values and outliers fundamentally mitigates their detrimental impact on ML model performance, ensuring robust training and reliable predictions.

Missing values (e.g., null, NaN) and outliers (data points significantly different from others) are common data quality issues that can negatively affect model training and lead to biased or inaccurate predictions.

Key Strategies for Handling Missing Values:

• Deletion:
  • Listwise Deletion (Row Deletion): Remove entire rows with any missing values. Simple but can lead to significant data loss if many rows have missing data.
  • Pairwise Deletion: Use all available data for each calculation (e.g., for correlation), ignoring missing values for specific pairs. Can lead to inconsistent results.
• Imputation (Filling Missing Values):
  • Mean/Median/Mode Imputation: Replace missing values with the mean, median, or mode of the column. Simple, but reduces variance and can introduce bias.
  • Forward/Backward Fill: Fill missing values using the previous or next valid observation (common for time series).
  • Regression/ML-based Imputation: Use other features to predict and fill missing values (more sophisticated but can be complex).
  • Constant Value Imputation: Replace with a specific constant (e.g., 0, -1, or a placeholder string).
• Indicator Variable: Create a new binary column to indicate whether a value was missing.

Key Strategies for Handling Outliers:

• Detection Methods:
  • Statistical Methods: Z-score, IQR (Interquartile Range) method.
  • Visualization: Box plots, scatter plots.
  • ML-based Methods: Isolation Forest, One-Class SVM.
• Treatment Methods:
  • Capping/Winsorization: Capping extreme values at a certain percentile (e.g., 5th and 95th percentile).
  • Transformation: Applying logarithmic or square root transformations to reduce the impact of extreme values.
  • Binning: Grouping numerical values into bins, which can smooth out the effect of outliers.
  • Deletion: Removing outlier records (only if they are rare and clearly erroneous).
  • Algorithm Choice: Some algorithms (e.g., tree-based models like Random Forest, XGBoost) are more robust to outliers than others (e.g., linear models, K-Means).

AWS Tools:

• SageMaker Data Wrangler provides built-in transformations for various imputation and outlier handling techniques.
• SageMaker Processing Jobs or Glue ETL Jobs for custom Python/Spark scripts implementing these strategies at scale.

Scenario: You are analyzing a financial dataset for fraud detection. Some transactions have missing amounts, and a few have exceptionally high values that seem anomalous. You need to prepare this data for a classification model.

Reflection Question: How do strategies like imputation (mean/median for missing values) and capping (for outliers) fundamentally mitigate their detrimental impact on ML model performance, ensuring robust training and reliable predictions by preventing model misinterpretation of corrupted data?

💡 Tip: The choice of how to handle missing values and outliers is context-dependent. There is no single "best" method; it depends on the data and the model.