Learn

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.

### Instructions

Find another dataset for binary classification from Kaggle or take a look at `sklearn`

‘s breast cancer dataset.
Use `sklearn`

to build your own Logistic Regression model on the data and make some predictions. Which features are most important in the model you build?

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