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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,

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LRDPRDX commented Mar 3, 2017

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


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hack-r commented Aug 25, 2017

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


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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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DiWuDi commented Oct 1, 2018

Just click the "raw" on the top right corner, and copy from there. Then past it to your Jupyter cell.

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