Linear Discriminant Analysis With Python
Linear Discriminant Analysis is a linear classification machine learning algorithm.
The algorithm involves developing a probabilistic model per class based on the specific distribution of observations for each input variable. A new example is then classified by calculating the conditional probability of it belonging to each class and selecting the class with the highest probability.
As such, it is a relatively simple probabilistic classification model that makes strong assumptions about the distribution of each input variable, although it can make effective predictions even when these expectations are violated (e.g. it fails gracefully).
In this tutorial, you will discover the Linear Discriminant Analysis classification machine learning algorithm in Python.
After completing this tutorial, you will know:
- The Linear Discriminant Analysis is a simple linear machine learning algorithm for classification.
- How to fit, evaluate, and make predictions with the Linear Discriminant Analysis model with Scikit-Learn.
- How to tune the hyperparameters of the Linear Discriminant Analysis algorithm on a given dataset.
Let’s get started.
