# Using Linear Discriminant Analysis For Dimensionality Reduction

## Preliminaries

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

## Load Iris Data

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

## Create A Linear

# 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

# 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

## View the ratio of explained variance lda.explained_variance_ratio_

array([ 0.99147248])

### Find an error or bug?

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