基于逻辑回归绘制分类区域

Question

让我们考虑以下数据：

from sklearn.linear_model import LogisticRegression
from sklearn import datasets
iris = datasets.load_iris()
X = iris.data[:, :2]  # we only take the first two features.
y = iris.target

我想在该数据集上创建逻辑回归，然后创建显示分类区域的图。所以我用：

model = LogisticRegression(solver='liblinear', random_state=0)
est=model.fit(X, y)
plt.scatter(X[:, 0], X[:, 1], c=est.predict(X))
plt.show()

但是如何使其看起来像下面的那个？

修改

我在下面创建了情节，但我仍然不知道如何将特定的情节更改为正方形，x'is和创建图例。您知道怎么做吗？我知道我必须对marker='s'和marker='x'做些什么，但是它会更改所有图像，并且我只想更改特定的分类。

print(__doc__)


import numpy as np
import matplotlib.pyplot as plt
from sklearn.linear_model import LogisticRegression
from sklearn import datasets

# import some data to play with
iris = datasets.load_iris()
X = iris.data[:, :2]  # we only take the first two features.
Y = iris.target

logreg = LogisticRegression(C=1e5)

# Create an instance of Logistic Regression Classifier and fit the data.
logreg.fit(X, Y)

# Plot the decision boundary. For that, we will assign a color to each
# point in the mesh [x_min, x_max]x[y_min, y_max].
x_min, x_max = X[:, 0].min() - .5, X[:, 0].max() + .5
y_min, y_max = X[:, 1].min() - .5, X[:, 1].max() + .5
h = .02  # step size in the mesh
xx, yy = np.meshgrid(np.arange(x_min, x_max, h), np.arange(y_min, y_max, h))
Z = logreg.predict(np.c_[xx.ravel(), yy.ravel()])

# Put the result into a color plot
Z = Z.reshape(xx.shape)
plt.figure(1, figsize=(4, 3))
plt.pcolormesh(xx, yy, Z, cmap=plt.cm.Paired)

# Plot also the training points
plt.scatter(X[:, 0], X[:, 1], c=Y, edgecolors='k', cmap=plt.cm.Paired)
plt.xlabel('Sepal length')
plt.ylabel('Sepal width')

plt.xlim(xx.min(), xx.max())
plt.ylim(yy.min(), yy.max())
plt.xticks(())
plt.yticks(())
plt.show()