# SVM Maximum Margin Separating Hyperplane in Scikit-learn

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### Version¶

In [1]:

```
import sklearn
sklearn.__version__
```

Out[1]:

### Imports¶

In [2]:

```
print(__doc__)
import plotly.plotly as py
import plotly.graph_objs as go
import numpy as np
from sklearn import svm
```

### Calculations¶

In [3]:

```
# we create 40 separable points
np.random.seed(0)
X = np.r_[np.random.randn(20, 2) - [2, 2], np.random.randn(20, 2) + [2, 2]]
Y = [0] * 20 + [1] * 20
# fit the model
clf = svm.SVC(kernel='linear')
clf.fit(X, Y)
# get the separating hyperplane
w = clf.coef_[0]
a = -w[0] / w[1]
xx = np.linspace(-5, 5)
yy = a * xx - (clf.intercept_[0]) / w[1]
# plot the parallels to the separating hyperplane that pass through the
# support vectors
b = clf.support_vectors_[0]
yy_down = a * xx + (b[1] - a * b[0])
b = clf.support_vectors_[-1]
yy_up = a * xx + (b[1] - a * b[0])
```

### Plot Results¶

Plot the parallels to the separating hyperplane that pass through the support vectors