Underfitting Diagnosis and Mitigation

7 posts · Oct 2023 – May 2026

What this trail covers

Hex has evolved its machine learning capabilities by detailing the diagnosis and mitigation of underfitting and overfitting, including strategies for feature selection to prevent overfitting. This involves understanding the bias-variance tradeoff, systematic approaches to fixing issues, and careful feature selection to improve model generalization. This post details using autoencoders for feature selection, a technique that leverages neural networks to learn compressed representations of data, t

All Hex trails

May 2026Feature Engineering in Data Science: A Complete GuideThis post provides a comprehensive guide to feature engineering in data science, detailing techniques for numerical, categorical, datetime, and text data. It emphasizes the importance of feature engineering over algorithmic choice for model performance and discusses pitfalls like data leakage and non-reproducible pipelines. It also introduces automated feature engineering and future directions. The post aligns with the existing 'underfitting-diagnosis-and-mitigation' thread by focusing on how feature representation directly impacts model performance and generalization, offering practical techniques and best practices for creating effective features.
May 2026What Is Underfitting in Machine Learning?This post details what underfitting looks like in practice, explaining it as a model that hasn't learned relevant patterns and performs poorly on both training and test sets, characterized by high bias. It contrasts underfitting with overfitting by explaining the bias-variance tradeoff and how learning curves illustrate this. The post highlights why underfitting is harder to catch than overfitting, often leading to anti-patterns like "just needing more data" or cycling through algorithms without auditing features. It outlines a structured iteration loop for fixing underfitting: increasing model capacity, reducing regularization, engineering better features, improving data quality, and tuning hyperparameters. It also discusses the dangers of AI analytics meeting underfit models, leading to plausible but meaningless outputs, and emphasizes the importance of integrating model diagnostics into the explanation pipeline. Finally, it notes that fixing underfitting often exposes overfitting, and managing this tension is the core job of ML practitioners.
May 2026Feature leakage in ML: Detect, Prevent, and Fix ItThis post details feature leakage in ML, a critical failure mode where information not available at prediction time contaminates training data, leading to inflated validation metrics and poor production performance. It categorizes leakage into target leakage, train-test contamination, and temporal leakage. The post provides practical strategies for detecting leakage during EDA, such as suspicious feature-target correlations, near-perfect naive baselines, dominant feature importance, differential null rates, and timestamp mismatches. It also outlines pipeline-level fixes like using scikit-learn Pipelines for preprocessing, TimeSeriesSplit for temporal data, and GroupKFold for group-based validation. A new vector of leakage introduced by AI-generated code is also discussed, emphasizing the need for careful auditing of AI-assisted ML workflows.
Jan 2026What is overfitting? Causes, detection & preventionThis post details the causes, detection, and prevention of overfitting in machine learning models. It explains the bias-variance tradeoff, the problems caused by overfitting (clinical risks, operational noise, eroding stakeholder confidence), and common causes like insufficient data, noisy data, feature engineering mistakes, and inadequate regularization. Detection methods like learning curves, cross-validation, validation curves, early stopping, and production monitoring are discussed. Prevention strategies include regularization (L1, L2, dropout), data augmentation, early stopping, and architectural choices. The post highlights how Hex's platform supports these practices by enabling experimentation with regularization, visualization of learning curves, and tracking performance across iterations.
Dec 2025Overfitting vs Underfitting in ML: Causes, Diagnosis, and FixesThis post details the causes, diagnosis, and fixes for overfitting and underfitting in machine learning models. It explains the bias-variance tradeoff and provides practical strategies for addressing these issues, including regularization, early stopping, data augmentation, cross-validation, and ensemble methods for overfitting, and increasing model complexity, adding critical features, and adjusting regularization for underfitting. It also discusses learning curves and how to iterate faster on model evaluation.
Advertisement
Oct 2023How Overfitting Ruins Your Feature SelectionThis post details how overfitting can ruin feature selection by causing inconsistent feature importance rankings, discarding relevant features, selecting irrelevant features, increasing sensitivity to data variability, and ultimately leading to poor generalization. It provides an example using a decision tree and discusses strategies to prevent overfitting in feature selection, such as using regularization techniques (L1, L2, Elastic Net) and model constraints.
Oct 2023Using Autoencoders for Feature SelectionThis post introduces autoencoders as a method for feature selection in machine learning. It explains the architecture of autoencoders (encoder, latent space, decoder) and their application in dimensionality reduction and feature extraction. The post contrasts autoencoders with traditional feature selection methods, highlighting their ability to handle non-linear relationships and automate feature engineering. A practical example using the Iris dataset demonstrates the steps involved: data preprocessing, building an autoencoder with Keras, and using the latent space representation for feature selection.