决策树的准确性评分
决策树的准确性评分
提问于 2022-05-30 17:20:51
第1部分
decision_tree.fit(X_train, y_train)
Y_val = decision_tree.predict(X_val)
acc_decision_tree_train = round(decision_tree.score(X_train, y_train) * 100, 2)
acc_decision_tree_train第2部分
acc_decision_tree_val = round(decision_tree.score(X_val, y_val) * 100, 2)
print('accuracy:', acc_decision_tree_val)第三部分
con_mat=confusion_matrix(y_val, Y_pred_val)
sns.heatmap(con_mat,annot=True,annot_kws= {"size":20},cmap="viridis")
plt.show()第四部分
acc_decision_tree_test = round(decision_tree.score(X_test, y_test) * 100, 2)
print('accuracy:', acc_decision_tree_test)
Y_pred_test = decision_tree.predict(X_test)
以上代码分为4部分。
Q1 ->在列车上的拟合和Val上的预测，在这一步骤中，模型通过对训练数据x_train的拟合来学习，但是我们并没有进行任何预测来获得y_train，所以在这种情况下，如何才能得到列车预测的准确性评分(模型正在学习，对吗？)Q2 ->In第2部分，正如我们前面所做的"Y_val = decision_tree.predict(X_val)“一样，我们可以计算验证的分数，这个分数是否与混淆矩阵中的准确性度量相同。Q3->在第4部分中，我只是要求测试数据的准确性评分，但是我没有对测试数据执行任何“预测”，但是它如何能够给我评分而不进行预测。
如果有什么不清楚的地方，请告诉我，谢谢:)
回答 1
Stack Overflow用户
发布于 2022-05-31 13:26:41
我调整了代码片段，我得到了虹膜数据集的准确性。
from sklearn.datasets import load_iris
from sklearn.model_selection import cross_val_score
from sklearn.tree import DecisionTreeClassifier
from sklearn.model_selection import train_test_split
from sklearn.metrics import confusion_matrix
import matplotlib.pyplot as plt
import seaborn as sns
decision_tree = DecisionTreeClassifier(random_state=0)
iris = load_iris()
X_train, X_test, y_train, y_test= train_test_split(iris.data, iris.target, test_size= 0.25, random_state=0)
X_train, X_val, y_train, y_val= train_test_split(X_train, y_train, test_size= 0.25, random_state=0)
#score = cross_val_score(decision_tree, iris.data, iris.target, cv=10)
decision_tree.fit(X_train, y_train)
y_pred_val = decision_tree.predict(X_val)
acc_decision_tree_train = round(decision_tree.score(X_train, y_train) * 100, 2)
print("acc_decision_tree_train ", acc_decision_tree_train)
acc_decision_tree_val = round(decision_tree.score(X_val, y_val) * 100, 2)
print('accuracy:', acc_decision_tree_val)
con_mat=confusion_matrix(y_val, y_pred_val)
sns.heatmap(con_mat,annot=True,annot_kws= {"size":20},cmap="viridis")
plt.show()
acc_decision_tree_test = round(decision_tree.score(X_test, y_test) * 100, 2)
print('accuracy:', acc_decision_tree_test)
y_pred_test = decision_tree.predict(X_test)输出：
acc_decision_tree_train 100.0
accuracy: 100.0
accuracy: 97.37
页面原文内容由Stack Overflow提供。腾讯云小微IT领域专用引擎提供翻译支持
原文链接：
https://stackoverflow.com/questions/72437992
复制相关文章
相似问题
腾讯云开发者
