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# Selecting The Best Number Of Components For LDA

In scikit-learn, LDA is implemented using `LinearDiscriminantAnalysis`

includes a parameter, `n_components`

indicating the number of features we want returned. To figure out what argument value to use with `n_components`

(e.g. how many parameters to keep), we can take advantage of the fact that `explained_variance_ratio_`

tells us the variance explained by each outputted feature and is a sorted array.

Specifically, we can run `LinearDiscriminantAnalysis`

with `n_components`

set to `None`

to return ratio of variance explained by every component feature, then calculate how many components are required to get above some threshold of variance explained (often 0.95 or 0.99).

## 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
```

## Run Linear Discriminant Analysis

```
# Create and run an LDA
lda = LinearDiscriminantAnalysis(n_components=None)
X_lda = lda.fit(X, y)
```

## Create List Of Explained Variances

```
# Create array of explained variance ratios
lda_var_ratios = lda.explained_variance_ratio_
```

## Create Function Calculating Number Of Components Required To Pass Threshold

```
# Create a function
def select_n_components(var_ratio, goal_var: float) -> int:
# Set initial variance explained so far
total_variance = 0.0
# Set initial number of features
n_components = 0
# For the explained variance of each feature:
for explained_variance in var_ratio:
# Add the explained variance to the total
total_variance += explained_variance
# Add one to the number of components
n_components += 1
# If we reach our goal level of explained variance
if total_variance >= goal_var:
# End the loop
break
# Return the number of components
return n_components
```

## Run Function

```
# Run function
select_n_components(lda_var_ratios, 0.95)
```

```
1
```