Precision is the fraction of positive predictions that are actually positive. Recall is the fraction of actual positives that were correctly identified. F1 is their harmonic mean. Which matters depends…
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#evaluation
What is the difference between precision recall and F1 score — and when does each matter?
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Can you explain how to use Scikit-learn for creating a train-test split of your data, and why this is important?
In Scikit-learn, you can use the train_test_split function to divide your dataset into training and testing subsets. This is crucial because it helps to evaluate the model’s performance on unseen…
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How do you evaluate the quality of an LLM-powered application in production?
LLM application quality requires a multi-layered evaluation strategy: offline evals (automated benchmarks using LLM-as-judge) online monitoring (latency cost error rates) and human evaluation for quality calibration. There is no single…
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What is a training set validation set and test set — and why do you need all three?
Training set is used to fit the model. Validation set is used to tune hyperparameters and select the best model. Test set is held out completely and used only once…
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