Congratulations! You just learned how a logistic regression model works and how to fit one to a dataset. Here are some of the things you learned:

• Logistic regression is used to perform binary classification.
• Logistic regression is an extension of linear regression where we use a logit link function to fit a sigmoid curve to the data, rather than a line.
• We can use the coefficients from a logistic regression model to estimate the log odds that a datapoint belongs to the positive class. We can then transform the log odds into a probability.
• The coefficients of a logistic regression model can be used to estimate relative feature importance.
• A classification threshold is used to determine the probabilistic cutoff for where a data sample is classified as belonging to a positive or negative class. The default cutoff in sklearn is 0.5.
• We can evaluate a logistic regression model using a confusion matrix or summary statistics such as accuracy, precision, recall, and F1 score.