最优分箱,信息增益 代码
时间: 2023-11-12 16:52:45 浏览: 42
下面是一个使用信息增益进行最优分箱的示例代码:
```python
import numpy as np
import pandas as pd
from sklearn.tree import DecisionTreeClassifier
def calculate_entropy(data):
# 计算数据集的熵
classes = np.unique(data)
entropy = 0
for c in classes:
p = len(data[data == c]) / len(data)
entropy -= p * np.log2(p)
return entropy
def calculate_information_gain(data, feature, target):
# 计算特征对目标变量的信息增益
feature_values = np.unique(data[feature])
entropy_total = calculate_entropy(data[target])
gain = 0
for value in feature_values:
subset = data[data[feature] == value]
entropy_subset = calculate_entropy(subset[target])
weight = len(subset) / len(data)
gain += weight * entropy_subset
information_gain = entropy_total - gain
return information_gain
def binning_optimal(data, feature, target, bins):
# 使用信息增益进行最优分箱
data_copy = data.copy()
data_copy['bucket'] = pd.qcut(data_copy[feature], bins)
grouped = data_copy.groupby('bucket', as_index=False)
aggregated = pd.DataFrame()
aggregated['min_value'] = grouped[feature].min()
aggregated['max_value'] = grouped[feature].max()
aggregated['count'] = grouped.size()
aggregated['total'] = grouped[target].sum()
aggregated['bad'] = grouped[target].sum()
aggregated['good'] = grouped.size() - grouped[target].sum()
aggregated['event_rate'] = grouped[target].mean()
aggregated['non_event_rate'] = grouped.size() - grouped[target].mean()
aggregated['entropy'] = aggregated.apply(lambda x: calculate_entropy(np.array([x['good'], x['bad']])), axis=1)
aggregated['information_gain'] = aggregated['entropy'].shift(1) - aggregated['entropy']
# 选择最佳分箱
best_split = aggregated['information_gain'].idxmax()
split_value = aggregated.loc[best_split, 'max_value']
# 划分数据集
data[feature] = np.where(data[feature] <= split_value, 'Low', 'High')
return data
# 示例用法
data = pd.DataFrame({'age': [25, 30, 35, 40, 45, 50, 55, 60],
'income': [5000, 6000, 7000, 8000, 9000, 10000, 11000, 12000],
'target': [0, 1, 1, 0, 1, 0, 1, 0]})
data = binning_optimal(data, 'age', 'target', bins=2)
print(data)
```
这段代码使用了信息增益作为评估指标,将连续的特征"age"进行最优分箱。首先,计算数据集的熵和特征对目标变量的信息增益。然后,根据信息增益选择最佳分箱点,并将数据集根据分箱点进行划分。最后,输出划分后的数据集。
请注意,这只是一个示例代码,实际应用中可能需要进行更多的处理和优化,具体取决于数据和模型的要求。
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