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 are precision, recall and F1 score, and why can accuracy be misleading?
Precision is how many predicted positives were actually positive. Recall is how many actual positives the model found. F1 is their harmonic mean. On imbalanced data, a model can get high accuracy by always predicting the majority class while missing every case you care about.
BeginnerIn-depth4 min read - 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