This is documentation for an old release of Scikit-learn (version 1.4). Try the latest stable release (version 1.6) or development (unstable) versions.
Note
Go to the end to download the full example code or to run this example in your browser via JupyterLite or Binder
SGD: Penalties¶
Contours of where the penalty is equal to 1 for the three penalties L1, L2 and elastic-net.
All of the above are supported by SGDClassifier
and SGDRegressor
.

import matplotlib.pyplot as plt
import numpy as np
l1_color = "navy"
l2_color = "c"
elastic_net_color = "darkorange"
line = np.linspace(-1.5, 1.5, 1001)
xx, yy = np.meshgrid(line, line)
l2 = xx**2 + yy**2
l1 = np.abs(xx) + np.abs(yy)
rho = 0.5
elastic_net = rho * l1 + (1 - rho) * l2
plt.figure(figsize=(10, 10), dpi=100)
ax = plt.gca()
elastic_net_contour = plt.contour(
xx, yy, elastic_net, levels=[1], colors=elastic_net_color
)
l2_contour = plt.contour(xx, yy, l2, levels=[1], colors=l2_color)
l1_contour = plt.contour(xx, yy, l1, levels=[1], colors=l1_color)
ax.set_aspect("equal")
ax.spines["left"].set_position("center")
ax.spines["right"].set_color("none")
ax.spines["bottom"].set_position("center")
ax.spines["top"].set_color("none")
plt.clabel(
elastic_net_contour,
inline=1,
fontsize=18,
fmt={1.0: "elastic-net"},
manual=[(-1, -1)],
)
plt.clabel(l2_contour, inline=1, fontsize=18, fmt={1.0: "L2"}, manual=[(-1, -1)])
plt.clabel(l1_contour, inline=1, fontsize=18, fmt={1.0: "L1"}, manual=[(-1, -1)])
plt.tight_layout()
plt.show()
Total running time of the script: (0 minutes 0.263 seconds)
Related examples

Illustration of Gaussian process classification (GPC) on the XOR dataset
Illustration of Gaussian process classification (GPC) on the XOR dataset