For quant trading and research roles: regression, classification, overfitting, and model evaluation — with full solutions.
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Machine learning has moved from a niche skill to a standard part of quant interviews, especially for research and quant-trading seats. Expect questions on regression and classification fundamentals, the bias-variance trade-off, overfitting and regularisation, cross-validation, and how models behave on noisy financial data. The examples below sit at the difficulty these interviews tend to ask.
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Why practise these
Markets are noisy, and most of the difficulty in applying machine learning to trading is statistical judgement rather than model zoo knowledge: knowing when a backtest is fooling you, why a model that fits beautifully in-sample falls apart out-of-sample, and which validation scheme actually respects time. These questions test that judgement — the part interviewers care about far more than being able to recite architectures.
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