原始数据：
原数据地址
计算第一次决策如果
分别对在14天各个属性下是否进行施肥的统计情况且计算该属性的基尼指数，同一种属性不同表现的基尼指数表示为M，加权平均之后为节点的基尼指数，用N表示
天气：
#encoding = utf-8
import pandas as pd
base_file = pd.read_excel('Data.xlsx')
base_file.head(15)
#base_file.head()
Weather_Sunny = base_file[base_file['天气'] == '晴天']['是否施肥'].value_counts()
#print(Weather_Sunny)['否' '否' '否' '是' '是']
Weather_Rainy = base_file[base_file['天气'] == '雨天']['是否施肥'].value_counts()
Weather_Overcast = base_file[base_file['天气'] == '阴天']['是否施肥'].value_counts()
Weather_Overcast['否'] = 0
Weather_df = pd.Dataframe([
pd.Series([Weather_Sunny['是'], Weather_Sunny['否']], index = ['是', '否']),
pd.Series([Weather_Rainy['是'], Weather_Rainy['否']], index = ['是', '否']),
pd.Series([Weather_Overcast['是'], Weather_Overcast['否']], index = ['是', '否'])
], index=['晴天', '雨天', '阴天'])
Weather_df.head()
晴天：M1 = 2 * 2/5 * (1 - 2/5) = 0.444444445
雨天：M2 = 2 * 3/5 * (1 - 3/5) = 0.48
阴天：M3 = 0
N1 = 5/14 * M1 + 5/14 * M2 = 0.343
温度：
Hot = base_file[base_file['温度'] == '炎热']['是否施肥'].value_counts()
Cool = base_file[base_file['温度'] == '温']['是否施肥'].value_counts()
Cold = base_file[base_file['温度'] == '冷']['是否施肥'].value_counts()
Temperature_df = pd.Dataframe([
pd.Series([Hot['是'], Hot['否']], index = ['是', '否']),
pd.Series([Cool['是'], Cool['否']], index = ['是', '否']),
pd.Series([Cold['是'], Cold['否']], index = ['是', '否'])
], index = ['炎热', '温', '冷'])
Temperature_df.head()
炎热：M1 = 2 * 2/4 * (1 - 2/4) = 0.5
温 ： M2 = 2 * 2/6 * (1 - 2/6) = 0.44444445
冷： M3 = 2 * 3/4 * (1 - 3/4) = 0.375
N2 = 4/14 * M1 + 6/14 * M2 + 4/14 * M3 = 0.440
湿度：
Humidity_high = base_file[base_file['湿度'] == '高']['是否施肥'].value_counts()
Humidity_mid = base_file[base_file['湿度'] == '中']['是否施肥'].value_counts()
Humidity_df = pd.Dataframe([
pd.Series([Humidity_high['是'], Humidity_high['否']], index = ['是', '否']),
pd.Series([Humidity_mid['是'], Humidity_mid['否']], index = ['是', '否'])
], index = ['高', '中'])
Humidity_df.head()
高：M1 = 2 * 3/4 * (1 - 3/4) = 0.375
中：M2 = 2 * 6/7 * (1 - 6/7) = 0.245
N3 = 1/2 * M1 + 1/2 * M2 = 0.310
风力：
Wind_strong = base_file[base_file['风力'] == '强风']['是否施肥'].value_counts() Wind_weak = base_file[base_file['风力'] == '弱风']['是否施肥'].value_counts() Wind_df = pd.Dataframe([ pd.Series([Wind_strong['是'], Wind_strong['否']], index = ['是', '否']), pd.Series([Wind_weak['是'], Wind_weak['否']], index = ['是', '否']) ], index = ['强风', '弱风']) Wind_df.head()
强风：M1 = 2 * 3/6 * (1 - 3/6) = 0.5
弱风：M2 = 2 * 6/8 * (1 - 6/8) = 0.375
N4 = 6/14 * M1 + 8/14 * M2 = 0.429
因为N2 > N4 > N1 > N3，所以第一次决策应根据湿度来分类：
因为此次分类之后，然未出现叶子节点，所以需要分别对第二排的两个节点进行分类，过程与第一次决策类似，计算各个属性下是否进行施肥的统计情况且计算该属性的基尼指数
左右节点的数据分别如下：
base_file = pd.read_excel('Data.xlsx')
base_file.head(15)
Temperature_df_high = base_file[base_file['湿度'] == '高']
Temperature_df_high.head(14)
先对左边节点分析：
天气：
Weather_Sunny = Temperature_df_high[Temperature_df_high['天气'] == '晴天']['是否施肥'].value_counts()
Weather_Sunny['是'] = 0
#print(Weather_Sunny)['否' '否' '否' '是' '是']
Weather_Rainy = Temperature_df_high[Temperature_df_high['天气'] == '雨天']['是否施肥'].value_counts()
Weather_Overcast = Temperature_df_high[Temperature_df_high['天气'] == '阴天']['是否施肥'].value_counts()
Weather_Overcast['否'] = 0
Weather_df = pd.Dataframe([
pd.Series([Weather_Sunny['是'], Weather_Sunny['否']], index = ['是', '否']),
pd.Series([Weather_Rainy['是'], Weather_Rainy['否']], index = ['是', '否']),
pd.Series([Weather_Overcast['是'], Weather_Overcast['否']], index = ['是', '否'])
], index=['晴天', '雨天', '阴天'])
Weather_df.head()
晴天：M1 = 0
雨天：M2 = 0.5
阴天：M3 = 0
N1 = 2/7 * M2 = 0.143
温度：
Hot = Temperature_df_high[Temperature_df_high['温度'] == '炎热']['是否施肥'].value_counts()
Cool = Temperature_df_high[Temperature_df_high['温度'] == '温']['是否施肥'].value_counts()
Cold = Temperature_df_high[Temperature_df_high['温度'] == '冷']['是否施肥'].value_counts()
Cold['是'] = 0
Cold['否'] = 0
Temperature_df = pd.Dataframe([
pd.Series([Hot['是'], Hot['否']], index = ['是', '否']),
pd.Series([Cool['是'], Cool['否']], index = ['是', '否']),
pd.Series([Cold['是'], Cold['否']], index = ['是', '否'])
], index = ['炎热', '温', '冷'])
Temperature_df.head()
炎热：M1 = 0.44444444445
温： M2 = 0.5
冷： M3 = 0
N2 = 3/7 * M1 + 4/7 * M3 = 0.476
风力：
Wind_strong = Temperature_df_high[Temperature_df_high['风力'] == '强风']['是否施肥'].value_counts()
Wind_weak = Temperature_df_high[Temperature_df_high['风力'] == '弱风']['是否施肥'].value_counts()
Wind_df = pd.Dataframe([
pd.Series([Wind_strong['是'], Wind_strong['否']], index = ['是', '否']),
pd.Series([Wind_weak['是'], Wind_weak['否']], index = ['是', '否'])
], index = ['强风', '弱风'])
Wind_df.head()
强风：M1 = 0.44444444445
弱风：M2 =0.5
N3 = 3/7 * M1 + 4/7 * M2 = 0.476
N1 > N2 = N3
所以左边的节点来说应该根据天气情况来分类
对右边节点分析：
天气：
Weather_Sunny = Temperature_df_mid[Temperature_df_mid['天气'] == '晴天']['是否施肥'].value_counts() Weather_Sunny['否'] = 0 Weather_Rainy = Temperature_df_mid[Temperature_df_mid['天气'] == '雨天']['是否施肥'].value_counts() Weather_Overcast = Temperature_df_mid[Temperature_df_mid['天气'] == '阴天']['是否施肥'].value_counts() Weather_Overcast['否'] = 0 Weather_df = pd.Dataframe([ pd.Series([Weather_Sunny['是'], Weather_Sunny['否']], index = ['是', '否']), pd.Series([Weather_Rainy['是'], Weather_Rainy['否']], index = ['是', '否']), pd.Series([Weather_Overcast['是'], Weather_Overcast['否']], index = ['是', '否']) ], index=['晴天', '雨天', '阴天']) Weather_df.head()
晴天：M1 = 0
雨天：M2 = 0.444444444445
阴天：M3 = 0
N1 = 3/7 * M2 = 0.190
温度：
Hot = Temperature_df_mid[Temperature_df_mid['温度'] == '炎热']['是否施肥'].value_counts() Hot['否'] = 0 Cool = Temperature_df_mid[Temperature_df_mid['温度'] == '温']['是否施肥'].value_counts() Cool['否'] = 0 Cold = Temperature_df_mid[Temperature_df_mid['温度'] == '冷']['是否施肥'].value_counts() Temperature_df = pd.Dataframe([ pd.Series([Hot['是'], Hot['否']], index = ['是', '否']), pd.Series([Cool['是'], Cool['否']], index = ['是', '否']), pd.Series([Cold['是'], Cold['否']], index = ['是', '否']) ], index = ['炎热', '温', '冷']) Temperature_df.head()
炎热：M1 = 0
温： M2 = 0
冷： M3 = 2 * 3/4 * (1 - 3/4) = 0.375
N2 = 3/7 * M3 = 0.214
风力：
Wind_strong = Temperature_df_mid[Temperature_df_mid['风力'] == '强风']['是否施肥'].value_counts()
Wind_weak = Temperature_df_mid[Temperature_df_mid['风力'] == '弱风']['是否施肥'].value_counts()
Wind_weak['否'] = 0
Wind_df = pd.Dataframe([
pd.Series([Wind_strong['是'], Wind_strong['否']], index = ['是', '否']),
pd.Series([Wind_weak['是'], Wind_weak['否']], index = ['是', '否'])
], index = ['强风', '弱风'])
Wind_df.head()
强风：M1 = 2 * 2/3 * (1 - 2/3) = 0.44444445
弱风：M2 = 0
N3 = 3/7 * M2 = 0.190
N1 = N3 > N2
这里可以有两种分类决策方法，这里选择使用天气属性对右边节点进行分类，结合对左边节点的分析，对第二层的分类如下：
经过第二次分类之后，出现了叶子节点，只剩下两个节点需要继续分类，且只剩下温度和风力两个属性，下面是第二次分类之后的左右两个节点数据：
Weather_df_Rainy1 = base_file[base_file['湿度'] == '高'][base_file['天气'] == '雨天'] Weather_df_Rainy1.head() Weather_df_Rainy2 = base_file[base_file['湿度'] == '中'][base_file['天气'] == '雨天'] Weather_df_Rainy2.head()
对于左边节点：
温度：
Hot = Weather_df_Rainy1[Weather_df_Rainy1['温度'] == '炎热']['是否施肥'].value_counts()
Hot['是'] = 0
Hot['否'] = 0
Cool = Weather_df_Rainy1[Weather_df_Rainy1['温度'] == '温']['是否施肥'].value_counts()
Cold = Weather_df_Rainy1[Weather_df_Rainy1['温度'] == '冷']['是否施肥'].value_counts()
Cold['是'] = 0
Cold['否'] = 0
Temperature_df = pd.Dataframe([
pd.Series([Hot['是'], Hot['否']], index = ['是', '否']),
pd.Series([Cool['是'], Cool['否']], index = ['是', '否']),
pd.Series([Cold['是'], Cold['否']], index = ['是', '否'])
], index = ['炎热', '温', '冷'])
Temperature_df.head()
炎热：M1 = 0
温： M2 = 2 * 1/2 * (1 – 1/2) = 0.5
冷： M3 = 0
N1 = M2 = 0.5
风力：
Hot = Weather_df_Rainy1[Weather_df_Rainy1['温度'] == '炎热']['是否施肥'].value_counts() Hot['是'] = 0 Hot['否'] = 0 Cool = Weather_df_Rainy1[Weather_df_Rainy1['温度'] == '温']['是否施肥'].value_counts() Cold = Weather_df_Rainy1[Weather_df_Rainy1['温度'] == '冷']['是否施肥'].value_counts() Cold['是'] = 0 Cold['否'] = 0 Temperature_df = pd.Dataframe([ pd.Series([Hot['是'], Hot['否']], index = ['是', '否']), pd.Series([Cool['是'], Cool['否']], index = ['是', '否']), pd.Series([Cold['是'], Cold['否']], index = ['是', '否']) ], index = ['炎热', '温', '冷']) Temperature_df.head()
强风：M1 = 0
弱风：M2 = 0
N2 = 0
N1 > N2
所以左边的节点应用风力属性继续往后分类
对右边节点分析：
温度：
Hot = Weather_df_Rainy2[Weather_df_Rainy2['温度'] == '炎热']['是否施肥'].value_counts() Hot['是'] = 0 Hot['否'] = 0 Cool = Weather_df_Rainy2[Weather_df_Rainy2['温度'] == '温']['是否施肥'].value_counts() Cool['否'] = 0 Cold = Weather_df_Rainy2[Weather_df_Rainy2['温度'] == '冷']['是否施肥'].value_counts() Temperature_df = pd.Dataframe([ pd.Series([Hot['是'], Hot['否']], index = ['是', '否']), pd.Series([Cool['是'], Cool['否']], index = ['是', '否']), pd.Series([Cold['是'], Cold['否']], index = ['是', '否']) ], index = ['炎热', '温', '冷']) Temperature_df.head()
炎热：M1 = 0
温： M2 = 0
冷： M3 = 2 * 1/2 * (1 – 1/2) = 0.5
N1 = M3 = 0.5
风力：
Wind_strong = Weather_df_Rainy2[Weather_df_Rainy2['风力'] == '强风']['是否施肥'].value_counts() Wind_strong['是'] = 0 Wind_weak = Weather_df_Rainy2[Weather_df_Rainy2['风力'] == '弱风']['是否施肥'].value_counts() Wind_weak['否'] = 0 Wind_df = pd.Dataframe([ pd.Series([Wind_strong['是'], Wind_strong['否']], index = ['是', '否']), pd.Series([Wind_weak['是'], Wind_weak['否']], index = ['是', '否']) ], index = ['强风', '弱风']) Wind_df.head()
强风：M1 = 0
弱风：M2 = 0
N2 = 0
N1 > N2
所以右边边的节点应用风力属性继续往后分类，决策图如下：
可以看出第二次分类再经过风力的分类之后，此时决策树最后一排的节点全部变为了叶子节点，说明至此，分类完成。
