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# Helper function to plot a decision boundary.
# If you don't fully understand this function don't worry, it just generates the contour plot below.
def plot_decision_boundary(pred_func):
# Set min and max values and give it some padding
x_min, x_max = X[:, 0].min() - .5, X[:, 0].max() + .5
y_min, y_max = X[:, 1].min() - .5, X[:, 1].max() + .5
h = 0.01
# Generate a grid of points with distance h between them
xx, yy = np.meshgrid(np.arange(x_min, x_max, h), np.arange(y_min, y_max, h))
# Predict the function value for the whole gid
Z = pred_func(np.c_[xx.ravel(), yy.ravel()])
Z = Z.reshape(xx.shape)
# Plot the contour and training examples
plt.contourf(xx, yy, Z,
plt.scatter(X[:, 0], X[:, 1], c=y,

There is error because of the last line: Unexpected indent.

hack-r commented Aug 25, 2017

@BogdanBessit The error is in how you pasted his code, not in the function...

duhaime commented Dec 21, 2017

This code comes more or less from the Scikit docs, e.g. in their example of a KNN classifier

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