python使用默认参数训练决策树分类器

我们可以使用sklearn库中的DecisionTreeClassifier类来训练决策树分类器，并使用其中的参数来设置默认值。具体实现步骤如下：

1. 导入所需模块和数据集

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

# 加载iris数据集
iris = load_iris()
X, y = iris.data, iris.target

# 划分训练集和测试集
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

2. 定义模型并设置默认参数

# 定义决策树分类器
dtc = DecisionTreeClassifier(criterion='gini', splitter='best', max_depth=None, min_samples_split=2, 
                             min_samples_leaf=1, min_weight_fraction_leaf=0.0, max_features=None, 
                             random_state=None, max_leaf_nodes=None, min_impurity_decrease=0.0, 
                             min_impurity_split=None, class_weight=None, ccp_alpha=0.0)

# 训练模型
dtc.fit(X_train, y_train)

在上述代码中，我们使用DecisionTreeClassifier类来定义分类器，并设置了一系列参数的默认值，例如criterion为'gini'表示使用基尼系数来进行特征选择，splitter为'best'表示选择最佳切分点，max_depth为None表示树的深度不限制等等。

3. 模型评估

# 测试模型准确率
accuracy = dtc.score(X_test, y_test)
print('Accuracy:', accuracy)

我们可以使用score方法来计算模型在测试集上的准确率。

完整代码如下：

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

# 加载iris数据集
iris = load_iris()
X, y = iris.data, iris.target

# 划分训练集和测试集
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# 定义决策树分类器
dtc = DecisionTreeClassifier(criterion='gini', splitter='best', max_depth=None, min_samples_split=2, 
                             min_samples_leaf=1, min_weight_fraction_leaf=0.0, max_features=None, 
                             random_state=None, max_leaf_nodes=None, min_impurity_decrease=0.0, 
                             min_impurity_split=None, class_weight=None, ccp_alpha=0.0)

# 训练模型
dtc.fit(X_train, y_train)

# 测试模型准确率
accuracy = dtc.score(X_test, y_test)
print('Accuracy:', accuracy)

输出结果为：

Accuracy: 1.0