3.1 Visualization of the model predictions on our test set¶
In [16]:
plt.figure(figsize =(8,10))
plt.subplot(212)
plt.scatter(X_test[:,0], X_test[:,1], c=prediction_values[:,0], cmap=cm.coolwarm)
plt.title('Model predictions on our Test set')
plt.axis('equal');
3.2 We can now see how our model separates the classes.¶
In [17]:
xx = np.linspace(-20, 20, 400)
yy = np.linspace(-20, 20, 400)
gx, gy = np.meshgrid(xx, yy)
Z = model.predict(np.c_[gx.ravel(), gy.ravel()])
Z = Z.reshape(gx.shape)
plt.contourf(gx, gy, Z, cmap=plt.cm.coolwarm, alpha=0.8)
axes = plt.gca()
axes.set_xlim([-20, 20])
axes.set_ylim([-20, 20])
plt.grid('off')
plt.axis('off')
plt.scatter(X_test[:,0], X_test[:,1], c=prediction_values[:,0], cmap=cm.coolwarm)
plt.title('Model predictions on our Test set')
Out[17]:
