Title: Using Linear Discriminant Analysis For Dimensionality Reduction
Slug: lda_for_dimensionality_reduction
Summary: How to use linear discriminant analysis for dimensionality reduction using Python.
Date: 2017-09-13 12:00
Category: Machine Learning
Tags: Feature Engineering
Authors: Chris Albon

Preliminaries



In [1]:

    
# Load libraries
from sklearn import datasets
from sklearn.discriminant_analysis import LinearDiscriminantAnalysis

Load Iris Data



In [2]:

    
# Load the Iris flower dataset:
iris = datasets.load_iris()
X = iris.data
y = iris.target

Create A Linear



In [3]:

    
# Create an LDA that will reduce the data down to 1 feature
lda = LinearDiscriminantAnalysis(n_components=1)

# run an LDA and use it to transform the features
X_lda = lda.fit(X, y).transform(X)

View Results



In [4]:

    
# Print the number of features
print('Original number of features:', X.shape[1])
print('Reduced number of features:', X_lda.shape[1])









    



Original number of features: 4
Reduced number of features: 1

View Percentage Of Variance Retained By New Features



In [5]:

    
## View the ratio of explained variance
lda.explained_variance_ratio_









    Out[5]:





array([ 0.99147248])