Interview Q&A / Machine Learning
Machine Learning interview questions
Supervised vs unsupervised learning, overfitting, metrics and the classic models, explained simply.
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- What is overfitting and underfitting, and what is the bias-variance trade-off?
Overfitting means the model memorises the training data, including noise, so it does well in training but badly on new data. Underfitting means the model is too simple to learn the pattern at all. The bias-variance trade-off is the balance between these two errors.
BeginnerIn-depth4 min read - What is the difference between supervised, unsupervised and reinforcement learning?
Supervised learning trains on examples that come with the correct answer (labels). Unsupervised learning finds patterns in data with no labels, such as groups. Reinforcement learning trains an agent by trial and error, using rewards and penalties from an environment.
BeginnerIn-depth4 min read