决策树python代码不用sklearn
时间: 2024-08-01 21:00:43 浏览: 39
在Python中,如果不使用scikit-learn库构建决策树,可以使用内置的`copy`模块和`collections`模块来模拟基本的决策树算法。以下是一个简单的示例,它创建了一个递归函数来构建决策树:
```python
class DecisionTreeNode:
def __init__(self, feature=None, value=None, left=None, right=None, *, is_leaf=False, prediction=None):
self.feature = feature
self.value = value
self.left = left
self.right = right
self.is_leaf = is_leaf
self.prediction = prediction
def gini_impurity(data, labels):
# 计算基尼不纯度
unique_labels, counts = np.unique(labels, return_counts=True)
impurity = sum((count / len(labels)) * (1 - count / len(labels)) for count in counts)
return impurity
def entropy(data, labels):
# 计算熵
_, counts = np.unique(labels, return_counts=True)
probabilities = counts / len(labels)
entropy = -sum(prob * np.log2(prob) for prob in probabilities if prob > 0)
return entropy
def find_best_split(data, features, labels):
best_gini = float('inf')
best_feature_index = None
best_value = None
for i, feature in enumerate(features):
splits = data[feature].unique()
for val in splits:
left_data = data[data[feature] != val]
right_data = data[data[feature] == val]
if len(left_data) == 0 or len(right_data) == 0:
continue
gini = gini_impurity(labels[left_data], left_data[labels])
info_gain = entropy(labels, labels) - (len(left_data) / len(data)) * gini - (len(right_data) / len(data)) * gini
if info_gain < best_gini:
best_gini = info_gain
best_feature_index = i
best_value = val
return best_feature_index, best_value
# 示例用法
def build_tree(data, features, labels, depth=0, max_depth=5):
impurity_before_split = gini_impurity(labels, labels)
if depth >= max_depth or impurity_before_split == 0 or len(np.unique(labels)) <= 1:
leaf_node = DecisionTreeNode(is_leaf=True, prediction=np.argmax(np.bincount(labels)))
return leaf_node
best_feature_index, best_value = find_best_split(data, features, labels)
left_data = data[data[features[best_feature_index]] != best_value]
right_data = data[data[features[best_feature_index]] == best_value]
left_child = build_tree(left_data, features, labels, depth + 1, max_depth)
right_child = build_tree(right_data, features, labels, depth + 1, max_depth)
tree_node = DecisionTreeNode(feature=features[best_feature_index], value=best_value,
left=left_child, right=right_child)
return tree_node
# 使用示例
data = ... # 假设这是包含特征和标签的数据集
features = ... # 特征列名列表
labels = data['label_column'] # 假设标签列名为'label_column'
root = build_tree(data, features, labels)
```
注意,这只是一个基础版本的决策树,实际应用中你可能需要处理缺失值、剪枝等问题,并提供用户输入数据来进行预测。
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