Logistic Regression

The Machine Learning algorithm that I will be discussing is fairly easy to understand and grasp. A powerful Machine Learning algorithm is logistic regression. Although the name implies regression, it is actually a (supervised) classification algorithm. Most of the time, it is used for predicting binary classes from the logit function. There are also different forms of logistic regression that include multinomial and ordinal target variables. Here are some popular examples that you can expect to encounter in the real-world and not just academic setting.

Business use cases for logistic regression: Customer churn or no churn

This example predicts if a user of a product will or will not churn, meaning, they unsubscribe and drop themselves from the product. Possible features could include if they have low activity on the platform, failed to pay a fee, along with rates of specific lower activity. Email spam or not spam

You can be creative and imagine a lot of situations as a 0 or 1, but it is ultimately up to your entire dataset, business use case, and the impact that will determine if this algorithm is right for you and your project. You could try to predict something as random as a house or not house — using descriptive features to help classify your target variable, but depending on your business, you will learn to find more useful and applicable situations for logistic regression (i.e., financial — approved or not approved, healthcare — disease or no disease).

You can expect to encounter these evaluation metrics with logistic regression:

1. Accuracy

2. Precision

3. Recall

Also important is the ROC (receiver operating characteristic) curve and AUC (area under the curve) along with sensitivity and specificity.