3. Visualization¶
In [14]:
plt.figure(figsize=(10,5))
plt.subplot(121)
plt.scatter(X_train[:,0], X_train[:,1], c=y_train ,cmap=cm.coolwarm)
plt.title('Training set')
plt.axis('equal');
plt.subplot(122)
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');
In [15]:
xx = np.linspace(-2, 3, 40)
yy = np.linspace(-2, 3, 40)
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([-2, 3])
axes.set_ylim([-2, 3])
plt.scatter(X_test[:,0], X_test[:,1], c=prediction_values[:,0], cmap=cm.coolwarm)
plt.title('Model predictions on our Test set')
Out[15]:
