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当我使用 4 个功能时,我在训练模型时遇到问题。我能够使用 2 个首要功能来实施培训。但是当我使用 4 个功能时会遇到一些麻烦。

可疑代码在这里:

from sklearn.datasets import load_iris
from sklearn.tree import DecisionTreeClassifier

iris = load_iris()
X = iris.data[:,:]
y = iris.target

tree_clf = DecisionTreeClassifier(criterion='entropy', max_depth=4, random_state=42)
tree_clf.fit(X, y)
from matplotlib.colors import ListedColormap

def plot_decision_boundary(clf, X, y, axes=[0, 10, 0, 5], iris=True, legend=False,     plot_training=True):
x1s = np.linspace(axes[0], axes[1], 100)
x2s = np.linspace(axes[2], axes[3], 100)

x1, x2 = np.meshgrid(x1s, x2s)
X_new = np.c_[x1.ravel(), x2.ravel()]
y_pred = clf.predict(X_new).reshape(x1.shape)
custom_cmap = ListedColormap(['#fafab0','#9898ff','#a0faa0'])
plt.contourf(x1, x2, y_pred, alpha=0.3, cmap=custom_cmap)
if not iris:
    custom_cmap2 = ListedColormap(['#7d7d58','#4c4c7f','#507d50'])
    plt.contour(x1, x2, y_pred, cmap=custom_cmap4, alpha=0.8)
if plot_training:
    plt.plot(X[:, 0][y==0], X[:, 1][y==0], "yo", label="Iris-Setosa")
    plt.plot(X[:, 0][y==1], X[:, 1][y==1], "bs", label="Iris-Versicolor")
    plt.plot(X[:, 0][y==2], X[:, 1][y==2], "g^", label="Iris-Virginica")
    plt.axis(axes)
if iris:
    plt.xlabel("Petal length", fontsize=14)
    plt.ylabel("Petal width", fontsize=14)
else:
    plt.xlabel(r"$x_1$", fontsize=18)
    plt.ylabel(r"$x_2$", fontsize=18, rotation=0)
if legend:
    plt.legend(loc="lower right", fontsize=14)

plt.figure(figsize=(8, 4))
plot_decision_boundary(tree_clf, X, y)
plt.plot([2.45, 2.45], [0, 3], "k-", linewidth=2)
plt.plot([2.45, 7.5], [1.75, 1.75], "k--", linewidth=2)
plt.plot([4.95, 4.95], [0, 1.75], "k:", linewidth=2)
plt.plot([4.85, 4.85], [1.75, 3], "k:", linewidth=2)
plt.text(1.40, 1.0, "Depth=0", fontsize=15)
plt.text(3.2, 1.80, "Depth=1", fontsize=13)
plt.text(4.05, 0.5, "(Depth=2)", fontsize=11)

任何人都可以帮忙,好吗?

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