绘制多项式和一对多逻辑回归#
绘制多项式和一对多逻辑回归的决策面。与三个一对多 (OVR) 分类器相对应的超平面由虚线表示。
training score : 0.995 (multinomial)
training score : 0.976 (ovr)
# Authors: Tom Dupre la Tour <[email protected]>
# License: BSD 3 clause
import matplotlib.pyplot as plt
import numpy as np
from sklearn.datasets import make_blobs
from sklearn.inspection import DecisionBoundaryDisplay
from sklearn.linear_model import LogisticRegression
from sklearn.multiclass import OneVsRestClassifier
# make 3-class dataset for classification
centers = [[-5, 0], [0, 1.5], [5, -1]]
X, y = make_blobs(n_samples=1000, centers=centers, random_state=40)
transformation = [[0.4, 0.2], [-0.4, 1.2]]
X = np.dot(X, transformation)
for multi_class in ("multinomial", "ovr"):
clf = LogisticRegression(solver="sag", max_iter=100, random_state=42)
if multi_class == "ovr":
clf = OneVsRestClassifier(clf)
clf.fit(X, y)
# print the training scores
print("training score : %.3f (%s)" % (clf.score(X, y), multi_class))
_, ax = plt.subplots()
DecisionBoundaryDisplay.from_estimator(
clf, X, response_method="predict", cmap=plt.cm.Paired, ax=ax
)
plt.title("Decision surface of LogisticRegression (%s)" % multi_class)
plt.axis("tight")
# Plot also the training points
colors = "bry"
for i, color in zip(clf.classes_, colors):
idx = np.where(y == i)
plt.scatter(X[idx, 0], X[idx, 1], c=color, edgecolor="black", s=20)
# Plot the three one-against-all classifiers
xmin, xmax = plt.xlim()
ymin, ymax = plt.ylim()
if multi_class == "ovr":
coef = np.concatenate([est.coef_ for est in clf.estimators_])
intercept = np.concatenate([est.intercept_ for est in clf.estimators_])
else:
coef = clf.coef_
intercept = clf.intercept_
def plot_hyperplane(c, color):
def line(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.show()
脚本总运行时间:(0 分钟 0.216 秒)
相关示例
在鸢尾花数据集上绘制多类 SGD
20newgroups 上的多类稀疏逻辑回归
支持向量机平局决胜示例
绘制分类概率