Explain Overfitting and Underfitting in Machine Learning

Q: Explain Overfitting and Underfitting in Machine Learning

This question evaluates understanding of overfitting and underfitting in machine learning, including recognition of their manifestations, the bias–variance tradeoff, and implications for model generalization in classical ML and computer vision contexts.

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Question

ML Fundamentals and Computer Vision: Core Concepts

Instructions

You are interviewing for a data science role focused on classical ML and computer vision. Answer the following concisely, defining terms and giving practical diagnostics and remedies.

Questions

Differentiate overfitting and underfitting. How do you detect and mitigate each?
What is data augmentation? Provide image-specific examples and note pitfalls.
Describe the main components and purposes of a Convolutional Neural Network (CNN).
How does a Recurrent Neural Network (RNN) process sequential data?
In a transformer encoder, detail the roles of positional embeddings, self-attention, residual connections, and feed-forward networks.
What is dropout and why does it help?
Compare bagging and boosting in terms of bias, variance, and algorithm behavior.

Explain Overfitting and Underfitting in Machine Learning

ML Fundamentals and Computer Vision: Core Concepts

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Explain Overfitting and Underfitting in Machine Learning

Overview

ML Fundamentals and Computer Vision: Core Concepts

Instructions

Questions

Solution

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