Can you explain how logistic regression handles binary classification, and how you would interpret the coefficients in the model? Additionally what metrics would you prioritise to evaluate the model?

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Logistic regression is a statistical model used for binary classification, interpreting coefficients and evaluating metrics.

Logistic regression is a statistical model that predicts the probability of a binary outcome based on one or more predictor variables.
It uses the logistic function to map the input features to a probability between 0 and 1.
The coefficients in the model represent the impact of each predictor variable on the log-odds of the outcome.
Interpreting the coefficients involves exponentiating them to get the odds ratios, which indicate how a one-unit change in the predictor affects the odds of the outcome.
Metrics like accuracy, precision, recall, F1 score, and ROC-AUC are commonly used to evaluate the performance of the logistic regression model.

Can you explain how logistic regression handles binary classification, and how you would interpret the coefficients in the model? Additionally what metrics would you prioritise to evaluate the model?

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