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# 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])
```

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