Logistic regression and linear regression are very similar, but the two have slightly different objectives. In linear regression, we try to predict losses in insurance claims. In logistic regression, we’re trying to predict categorical outcomes, otherwise known as classification. In other terms, logistic regression is the classification-based equivalent of linear regression.
In this course, you’ll learn the logistic regression method. You’ll learn how to interpret regression parameters, how to evaluate logistic regression models, and how to apply them.
Best of all, you’ll learn by doing — you’ll practice and get feedback directly in the browser. At the end of the course, you’ll combine your skills to complete a project to classify heart diseases.
- Describing a logistic regression model
- Building a logistic regression model and evaluating it based on the data
- Interpreting the results of a logistic regression model
- Using a logistic regression model for inference and prediction
Logistic Regression Modeling in Python [5 lessons]
- Calculate the accuracy of a logistic regression
- Calculate the sensitivity and specificity of a logistic regression
- Plot estimated probabilities against observed classes
- Calculate the positive and negative predictive probability of a logistic regression
Projects in this course
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