WEBVTT

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Model evaluation and optimization.

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In this video, we will understand importance of model evaluation,

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introduction to basic evaluation methods, why evaluate models.

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When we build a machine learning model, it's not enough to just train it.

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We need to evaluate it to check if it's actually making good predictions.

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Because without testing, we don't know if our model is accurate, fair, or useful.

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Evaluation for classification.

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If the task is to put things into groups like spam or not spam,

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we measure accuracy, precision, and recall.

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These tell us how well the model is sorting items.

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Evaluation for regression and cross validation.

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If the task is predicting numbers like house prices, we check errors.

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How far the predictions are from the real values?

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Smaller errors mean better models.

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Instead of testing just once, we test on different slices of data.

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This helps us be sure the model works well in general, not just on one data set.

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Improving model performance.

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If the results aren't good, we can add more data, try better features,

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or use stronger algorithms to make the model smarter.

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That's how we evaluate and improve models, so they don't just learn,

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but actually perform well in the real world.