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# Detecting Outliers

## Preliminaries

```
# Load libraries
import numpy as np
from sklearn.covariance import EllipticEnvelope
from sklearn.datasets import make_blobs
```

## Create Data

```
# Create simulated data
X, _ = make_blobs(n_samples = 10,
n_features = 2,
centers = 1,
random_state = 1)
# Replace the first observation's values with extreme values
X[0,0] = 10000
X[0,1] = 10000
```

## Detect Outliers

`EllipticEnvelope`

assumes the data is normally distributed and based on that assumption “draws” an ellipse around the data, classifying any observation inside the ellipse as an inlier (labeled as `1`

) and any observation outside the ellipse as an outlier (labeled as `-1`

). A major limitation of this approach is the need to specify a `contamination`

parameter which is the proportion of observations that are outliers, a value that we don’t know.

```
# Create detector
outlier_detector = EllipticEnvelope(contamination=.1)
# Fit detector
outlier_detector.fit(X)
# Predict outliers
outlier_detector.predict(X)
```

```
array([-1, 1, 1, 1, 1, 1, 1, 1, 1, 1])
```