Note

Go to the end to download the full example code. or to run this example in your browser via JupyterLite or Binder

Plot multi-class SGD on the iris dataset#

Plot decision surface of multi-class SGD on iris dataset. The hyperplanes corresponding to the three one-versus-all (OVA) classifiers are represented by the dashed lines.

Decision surface of multi-class SGD

# Authors: The scikit-learn developers
# SPDX-License-Identifier: BSD-3-Clause
importmatplotlib.pyplotasplt
importnumpyasnp
fromsklearnimport datasets
fromsklearn.inspectionimport DecisionBoundaryDisplay
fromsklearn.linear_modelimport SGDClassifier
# import some data to play with
iris = datasets.load_iris ()
# we only take the first two features. We could
# avoid this ugly slicing by using a two-dim dataset
X = iris.data[:, :2]
y = iris.target
colors = "bry"
# shuffle
idx = np.arange (X.shape[0])
np.random.seed (13)
np.random.shuffle (idx)
X = X[idx]
y = y[idx]
# standardize
mean = X.mean(axis=0)
std = X.std(axis=0)
X = (X - mean) / std
clf = SGDClassifier (alpha=0.001, max_iter=100).fit(X, y)
ax = plt.gca ()
DecisionBoundaryDisplay.from_estimator (
 clf,
 X,
 cmap=plt.cm.Paired,
 ax=ax,
 response_method="predict",
 xlabel=iris.feature_names[0],
 ylabel=iris.feature_names[1],
)
plt.axis ("tight")
# Plot also the training points
for i, color in zip(clf.classes_, colors):
 idx = (y == i).nonzero()
 plt.scatter (
 X[idx, 0],
 X[idx, 1],
 c=color,
 label=iris.target_names[i],
 edgecolor="black",
 s=20,
 )
plt.title ("Decision surface of multi-class SGD")
plt.axis ("tight")
# Plot the three one-against-all classifiers
xmin, xmax = plt.xlim ()
ymin, ymax = plt.ylim ()
coef = clf.coef_
intercept = clf.intercept_
defplot_hyperplane(c, color):
 defline(x0):
 return (-(x0 * coef[c, 0]) - intercept[c]) / coef[c, 1]
 plt.plot ([xmin, xmax], [line(xmin), line(xmax)], ls="--", color=color)
for i, color in zip(clf.classes_, colors):
 plot_hyperplane(i, color)
plt.legend ()
plt.show ()