docs: def plot_fitspace(self, name: str, X: np.ndarray, y: np.ndarray, clf: object) -> None:
          """ Plot 2dplane of fitspace """
          cmap_light = ListedColormap(['#FFAAAA', '#AAFFAA', '#AAAAFF'])
          cmap_bold = ListedColormap(['#FF0000', '#00FF00', '#0000FF'])
  
          # Plot the decision boundary. For that, we will assign a color to each
          # point in the mesh [x_min, m_max]x[y_min, y_max].
          h = 0.01  # Mesh step size
          x_min, x_max = X[:, 0].min() - 1, X[:, 0].max() + 1
          y_min, y_max = X[:, 1].min() - 1, X[:, 1].max() + 1
          xx, yy = np.meshgrid(np.arange(x_min, x_max, h),
                               np.arange(y_min, y_max, h))
          Z = clf.predict(np.c_[xx.ravel(), yy.ravel()])
  
          # Put the result into a color plot
          Z = Z.reshape(xx.shape)
          plt.figure()
          plt.pcolormesh(xx, yy, Z, cmap=cmap_light)
  
          # Plot also the training points
          plt.scatter(X[:, 0], X[:, 1], c=y, cmap=cmap_bold)
          plt.xlim(xx.min(), xx.max())
          plt.ylim(yy.min(), yy.max())
          plt.xlabel(r'$\log_{10}$ Noise')
          plt.ylabel(r'$\log_{10}$ Curvature')
          plt.savefig(name)