Scikit Learn LDA - Python

Scikit Learn LDA (Linear Discriminant Analysis) is a technique used for dimensionality reduction. It is a supervised learning algorithm that is used to classify data with two or more classes. It is a way of combining two or more variables into one for the purpose of classification.

Installation

To install Scikit Learn, use the following command:

pip install -U scikit-learn

Usage

To use Scikit Learn LDA, you first need to import it as follows:

from sklearn.discriminant_analysis import LinearDiscriminantAnalysis

Once it is imported, you can create an instance of the LDA class and fit your data to it:

lda = LinearDiscriminantAnalysis(n_components=2)
X_lda = lda.fit_transform(X, y)

Here, the n_components parameter specifies the number of dimensions that the data will be reduced to. The fit_transform method is used to fit the data to the LDA algorithm and return the transformed data.

Example

Here's an example of how to use Scikit Learn LDA:

from sklearn.datasets import load_iris
from sklearn.discriminant_analysis import LinearDiscriminantAnalysis

iris = load_iris()
X = iris.data
y = iris.target

lda = LinearDiscriminantAnalysis(n_components=2)
X_lda = lda.fit_transform(X, y)

print(X_lda[:5])
# Output: 
# [[-8.06179978,  0.30042062],
#  [-7.12868772, -0.78666043],
#  [-7.48982797, -0.26538449],
#  [-6.81320057, -0.67063107],
#  [-8.13230933,  0.51446253]]

In this example, we load the Iris dataset, which consists of 3 classes of 50 instances each. We then create an instance of the LDA class with 2 dimensions and fit the data to it. Finally, we print the first 5 transformed instances of the data.

Conclusion

Scikit Learn LDA is a powerful technique that can be used for dimensionality reduction and classification. It is easy to use and can be applied to a wide range of applications.