overfitting
AI, ML & GenAI interview questions tagged overfitting, across every topic.
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Concepts behind "overfitting"
The curriculum that explains the ideas these questions test.
Foundational
The Bias-Variance TradeoffA model's error breaks into bias (error from being too simple to capture the pattern, underfitting) and variance (error from being too sensitive to the training sample, overfitting). Cutting one often raises the other, so generalization comes down to finding the balance. It is the lens behind regularization, model-complexity choices, and ensembling. AI, ML, and GenAI engineer interviews probe it because diagnosing whether a model underfits or overfits, and acting on it, is the core debugging skill of ML.📊 Evaluation & ML Foundations
Foundational
Overfitting and RegularizationOverfitting is when a model learns the training data's noise rather than its signal, scoring well in training but failing on new data. You prevent it with more data, regularization (L1/L2, dropout, early stopping), simpler models, and data augmentation, and you detect it with a proper held-out validation set. The deeper trap is data leakage, which yields fake great offline numbers that collapse in production. AI, ML, and GenAI engineer interviews probe it because shipping an overfit or leaky model is one of the most common, expensive ML mistakes.📊 Evaluation & ML Foundations
Foundational
Decision Trees and Splitting CriteriaA decision tree recursively splits the feature space by choosing the split that most reduces impurity (Gini or entropy), producing a flowchart you can read top to bottom. Interviews probe trees because they reveal whether you understand impurity-based splitting, why depth is the bias-variance knob, and how a single high-variance tree becomes the building block for random forests and gradient boosting.📊 Evaluation & ML Foundations
Core
Fine-Tuning Hyperparameters and OverfittingChoosing to fine-tune is the easy part. The run succeeds or fails on a short list of dials: learning rate, epochs, effective batch size, LoRA rank and alpha, which modules you target, and max sequence length. Overfitting is the default outcome when those dials are set by copying a blog post, and its signature is a train loss that keeps falling while eval loss turns up. AI, ML, and GenAI interviews probe this because it separates people who have actually run a fine-tune from people who have only read about one.🧠 Foundations of LLMs & GenAISign in