决策树/范例一: Decision Tree Regression http://scikit-learn.org/stable/auto_examples/tree/plot_tree_regression.html 范例目的 此范例利用Decision Tree从数据中学习一组if-then-else决策规则,逼近加有杂讯的sine curve,因此它模拟出局部的线性迴归以近似sine curve。 若决策树深度越深(可由max_depth参数控制)...
plt.plot(X_test, y_2, color="yellowgreen", label="max_depth=4", linewidth=2) plt.plot(X_test, y_3, color="r", label="max_depth=8", linewidth=2) plt.xlabel("data") plt.ylabel("target") plt.title("Decision Tree Regression") plt.legend() plt.show() 从上面的测试可以看出随着...
plt.scatter(x,y,s=20,edgecolor='black',c='darkorange',label='data') plt.plot(xtest,y_1,c='red',linestyle='-',linewidth=1.5) plt.plot(xtest,y_2,c='green',linestyle='-.',linewidth=1.5) plt.legend(['regr_1','regr_2','origin']) plt.title('Decision Tree Regression') plt.sho...
plt.plot(X_test, y_1, color="cornflowerblue",label="max_depth=2", linewidth=2) plt.plot(X_test, y_2, color="yellowgreen", label="max_depth=5", linewidth=2) plt.xlabel("data") plt.ylabel("target") plt.title("Decision Tree Regression") plt.legend() plt.show() 可见,回归树学习...
from sklearn.tree import DecisionTreeRegressortree_regl = DecisionTreeRegressor(random_state=42, max_depth=2)tree_reg2 = DecisionTreeRegressor(random_state=42, max_depth=3)tree_regl.fit(X, y)tree_reg2.fit(X, y)def plot_regression_predictions(tree_reg, X, y, axes=[0, 1, -0.2, 1]...
训练决策树:通过DecisionTreeClassifier类创建一个决策树分类器实例,并使用fit方法将训练数据(特征集X_train和目标标签y_train)输入到模型中,从而训练出决策树。 可视化决策树:利用plot_tree函数,将训练好的决策树模型(clf)进行可视化。这个函数接受多个参数,如是否填充节点颜色(filled=True)、特征名称(feature_names=ir...
但是Adaboost的stump仅仅是按照准确率来了,而decision tree的标准是purity,纯净度。意思就是熵了。purifying的核心思想就是每次切割都尽可能让左子树和右子树中同类样本占得比例最大或者yn都很接近(regression),即错误率最小。比如说classifiacation问题中,如果左子树全是正样本,右子树全是负样本,那么它的纯净度就...
但是Adaboost的stump仅仅是按照准确率来了,而decision tree的标准是purity,纯净度。意思就是熵了。purifying的核心思想就是每次切割都尽可能让左子树和右子树中同类样本占得比例最大或者yn都很接近(regression),即错误率最小。比如说classifiacation问题中,如果左子树全是正样本,右子树全是负样本,那么它的纯净度就...
The code below plots a decision tree using scikit-learn. tree.plot_tree(clf); This is not the most interpretable tree yet. In addition to adding the code to allow you to save your image, the code below tries to make the decision tree more interpretable by adding in feature and class ...
# 需要导入模块: from sklearn.tree import DecisionTreeRegressor [as 别名]# 或者: from sklearn.tree.DecisionTreeRegressor importscore[as 别名]defplot_curve():# Defining our regression algorithmreg = DecisionTreeRegressor()# Fit our model using X and yreg.fit(X, y)print"Regressorscore: {:.4f...