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Try to 개발자 EthanJ의 성장 로그
scikit-learn Machine Learning DecisionTree 사이킷런 머신러닝 결정트리 본문
CS & DS/scikit-learn Machine Learning
scikit-learn Machine Learning DecisionTree 사이킷런 머신러닝 결정트리
EthanJ 2022. 11. 21. 14:46
scikit-learn Machine Learning DecisionTree
사이킷런 머신러닝 결정트리
Data Collection¶
In [1]:
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
In [2]:
# https://www.kaggle.com/datasets/ayessa/salary-prediction-classification
url = 'https://raw.githubusercontent.com/dev-EthanJ/scikit-learn_Machine_Learning/main/data/salary.csv'
df = pd.read_csv(url, skipinitialspace=True)
df.head()
Out[2]:
| age | workclass | education | education-num | marital-status | occupation | relationship | race | sex | capital-gain | capital-loss | hours-per-week | native-country | class | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 25 | Private | 11th | 7 | Never-married | Machine-op-inspct | Own-child | Black | Male | 0 | 0 | 40 | United-States | <=50K |
| 1 | 38 | Private | HS-grad | 9 | Married-civ-spouse | Farming-fishing | Husband | White | Male | 0 | 0 | 50 | United-States | <=50K |
| 2 | 28 | Local-gov | Assoc-acdm | 12 | Married-civ-spouse | Protective-serv | Husband | White | Male | 0 | 0 | 40 | United-States | >50K |
| 3 | 44 | Private | Some-college | 10 | Married-civ-spouse | Machine-op-inspct | Husband | Black | Male | 7688 | 0 | 40 | United-States | >50K |
| 4 | 18 | NaN | Some-college | 10 | Never-married | NaN | Own-child | White | Female | 0 | 0 | 30 | United-States | <=50K |
In [3]:
df.tail()
Out[3]:
| age | workclass | education | education-num | marital-status | occupation | relationship | race | sex | capital-gain | capital-loss | hours-per-week | native-country | class | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 48837 | 27 | Private | Assoc-acdm | 12 | Married-civ-spouse | Tech-support | Wife | White | Female | 0 | 0 | 38 | United-States | <=50K |
| 48838 | 40 | Private | HS-grad | 9 | Married-civ-spouse | Machine-op-inspct | Husband | White | Male | 0 | 0 | 40 | United-States | >50K |
| 48839 | 58 | Private | HS-grad | 9 | Widowed | Adm-clerical | Unmarried | White | Female | 0 | 0 | 40 | United-States | <=50K |
| 48840 | 22 | Private | HS-grad | 9 | Never-married | Adm-clerical | Own-child | White | Male | 0 | 0 | 20 | United-States | <=50K |
| 48841 | 52 | Self-emp-inc | HS-grad | 9 | Married-civ-spouse | Exec-managerial | Wife | White | Female | 15024 | 0 | 40 | United-States | >50K |
Data pre-processing¶
범주형 변수 처리: drop, 연속형 변수화, dummies, 대체 data 등
결측치 처리
Outlier > DecisionTree 구현에 영향 없으므로 무시
In [4]:
df.info()
<class 'pandas.core.frame.DataFrame'> RangeIndex: 48842 entries, 0 to 48841 Data columns (total 14 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 age 48842 non-null int64 1 workclass 46043 non-null object 2 education 48842 non-null object 3 education-num 48842 non-null int64 4 marital-status 48842 non-null object 5 occupation 46033 non-null object 6 relationship 48842 non-null object 7 race 48842 non-null object 8 sex 48842 non-null object 9 capital-gain 48842 non-null int64 10 capital-loss 48842 non-null int64 11 hours-per-week 48842 non-null int64 12 native-country 47985 non-null object 13 class 48842 non-null object dtypes: int64(5), object(9) memory usage: 5.2+ MB
Continous Data(연속형 변수)
age: 연령education-num: 교육년수capital-gain: 자산 증가량capital-loss: 자산 감소량hours-per-week: 주당 노동 신간
In [5]:
df.describe()
Out[5]:
| age | education-num | capital-gain | capital-loss | hours-per-week | |
|---|---|---|---|---|---|
| count | 48842.000000 | 48842.000000 | 48842.000000 | 48842.000000 | 48842.000000 |
| mean | 38.643585 | 10.078089 | 1079.067626 | 87.502314 | 40.422382 |
| std | 13.710510 | 2.570973 | 7452.019058 | 403.004552 | 12.391444 |
| min | 17.000000 | 1.000000 | 0.000000 | 0.000000 | 1.000000 |
| 25% | 28.000000 | 9.000000 | 0.000000 | 0.000000 | 40.000000 |
| 50% | 37.000000 | 10.000000 | 0.000000 | 0.000000 | 40.000000 |
| 75% | 48.000000 | 12.000000 | 0.000000 | 0.000000 | 45.000000 |
| max | 90.000000 | 16.000000 | 99999.000000 | 4356.000000 | 99.000000 |
- Outlier in
capital-gain,capital-loss$\rightarrow$ 평균 기반 모델 사용 or Data Manipulation하면 왜곡 발생
Categorical Data(범주형 변수)
workclass: 고용 형태education: 학력marital-status: 결혼 상태occupation: 직업relationship: 가족관계race: 인종sex: 성별native-country: 출신국가class: 연봉 범위
In [6]:
df.describe(include=['O'])
Out[6]:
| workclass | education | marital-status | occupation | relationship | race | sex | native-country | class | |
|---|---|---|---|---|---|---|---|---|---|
| count | 46043 | 48842 | 48842 | 46033 | 48842 | 48842 | 48842 | 47985 | 48842 |
| unique | 8 | 16 | 7 | 14 | 6 | 5 | 2 | 41 | 2 |
| top | Private | HS-grad | Married-civ-spouse | Prof-specialty | Husband | White | Male | United-States | <=50K |
| freq | 33906 | 15784 | 22379 | 6172 | 19716 | 41762 | 32650 | 43832 | 37155 |
In [7]:
df.describe(include='all')
Out[7]:
| age | workclass | education | education-num | marital-status | occupation | relationship | race | sex | capital-gain | capital-loss | hours-per-week | native-country | class | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| count | 48842.000000 | 46043 | 48842 | 48842.000000 | 48842 | 46033 | 48842 | 48842 | 48842 | 48842.000000 | 48842.000000 | 48842.000000 | 47985 | 48842 |
| unique | NaN | 8 | 16 | NaN | 7 | 14 | 6 | 5 | 2 | NaN | NaN | NaN | 41 | 2 |
| top | NaN | Private | HS-grad | NaN | Married-civ-spouse | Prof-specialty | Husband | White | Male | NaN | NaN | NaN | United-States | <=50K |
| freq | NaN | 33906 | 15784 | NaN | 22379 | 6172 | 19716 | 41762 | 32650 | NaN | NaN | NaN | 43832 | 37155 |
| mean | 38.643585 | NaN | NaN | 10.078089 | NaN | NaN | NaN | NaN | NaN | 1079.067626 | 87.502314 | 40.422382 | NaN | NaN |
| std | 13.710510 | NaN | NaN | 2.570973 | NaN | NaN | NaN | NaN | NaN | 7452.019058 | 403.004552 | 12.391444 | NaN | NaN |
| min | 17.000000 | NaN | NaN | 1.000000 | NaN | NaN | NaN | NaN | NaN | 0.000000 | 0.000000 | 1.000000 | NaN | NaN |
| 25% | 28.000000 | NaN | NaN | 9.000000 | NaN | NaN | NaN | NaN | NaN | 0.000000 | 0.000000 | 40.000000 | NaN | NaN |
| 50% | 37.000000 | NaN | NaN | 10.000000 | NaN | NaN | NaN | NaN | NaN | 0.000000 | 0.000000 | 40.000000 | NaN | NaN |
| 75% | 48.000000 | NaN | NaN | 12.000000 | NaN | NaN | NaN | NaN | NaN | 0.000000 | 0.000000 | 45.000000 | NaN | NaN |
| max | 90.000000 | NaN | NaN | 16.000000 | NaN | NaN | NaN | NaN | NaN | 99999.000000 | 4356.000000 | 99.000000 | NaN | NaN |
범주형 변수 전처리¶
class¶
In [8]:
df['class']
Out[8]:
0 <=50K
1 <=50K
2 >50K
3 >50K
4 <=50K
...
48837 <=50K
48838 >50K
48839 <=50K
48840 <=50K
48841 >50K
Name: class, Length: 48842, dtype: object
In [9]:
df['class'].value_counts()
Out[9]:
<=50K 37155 >50K 11687 Name: class, dtype: int64
In [10]:
class_data = df['class'].unique()
class_data
Out[10]:
array(['<=50K', '>50K'], dtype=object)
- 데이터 2가지 → 0, 1로 맵핑
In [11]:
df['class'] = df['class'].map({class_data[0]:0, class_data[1]:1})
print(df['class'].unique())
print(df['class'].isnull().sum())
[0 1] 0
sex¶
In [12]:
df['sex'].unique()
Out[12]:
array(['Male', 'Female'], dtype=object)
In [13]:
df['sex'].isnull().sum()
Out[13]:
0
In [14]:
df['sex'] = pd.get_dummies(df['sex'], drop_first=True)
df['sex'].head()
Out[14]:
0 1 1 1 2 1 3 1 4 0 Name: sex, dtype: uint8
범주형 변수 걸러내는 법
obj.dtype == 'object'
In [15]:
obj_list = [(col, df[col].nunique()) for col in df.columns if df[col].dtype == 'object']
obj_list
Out[15]:
[('workclass', 8),
('education', 16),
('marital-status', 7),
('occupation', 14),
('relationship', 6),
('race', 5),
('native-country', 41)]
In [16]:
for obj in obj_list:
if obj[1] > 10:
print(obj)
('education', 16)
('occupation', 14)
('native-country', 41)
- 범주형 데이터의 고유 data가 10개 이상 > 과다한 Data 개수
education, education-num¶
In [17]:
edu_sr = df['education'].value_counts()
edu_sr
Out[17]:
HS-grad 15784 Some-college 10878 Bachelors 8025 Masters 2657 Assoc-voc 2061 11th 1812 Assoc-acdm 1601 10th 1389 7th-8th 955 Prof-school 834 9th 756 12th 657 Doctorate 594 5th-6th 509 1st-4th 247 Preschool 83 Name: education, dtype: int64
In [18]:
edunum_sr = df['education-num'].value_counts()
edunum_sr
Out[18]:
9 15784 10 10878 13 8025 14 2657 11 2061 7 1812 12 1601 6 1389 4 955 15 834 5 756 8 657 16 594 3 509 2 247 1 83 Name: education-num, dtype: int64
In [19]:
for num in df['education-num'].unique():
print(num, df[df['education-num'] == num]['education'].unique())
7 ['11th'] 9 ['HS-grad'] 12 ['Assoc-acdm'] 10 ['Some-college'] 6 ['10th'] 15 ['Prof-school'] 4 ['7th-8th'] 13 ['Bachelors'] 14 ['Masters'] 16 ['Doctorate'] 3 ['5th-6th'] 11 ['Assoc-voc'] 5 ['9th'] 8 ['12th'] 2 ['1st-4th'] 1 ['Preschool']
각
education-num의 data 1개와education의 data 1개는 mapping된다.- 둘 중 하나의 컬럼
education삭제
- 둘 중 하나의 컬럼
In [20]:
df = df.drop('education', axis=1)
df.head()
Out[20]:
| age | workclass | education-num | marital-status | occupation | relationship | race | sex | capital-gain | capital-loss | hours-per-week | native-country | class | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 25 | Private | 7 | Never-married | Machine-op-inspct | Own-child | Black | 1 | 0 | 0 | 40 | United-States | 0 |
| 1 | 38 | Private | 9 | Married-civ-spouse | Farming-fishing | Husband | White | 1 | 0 | 0 | 50 | United-States | 0 |
| 2 | 28 | Local-gov | 12 | Married-civ-spouse | Protective-serv | Husband | White | 1 | 0 | 0 | 40 | United-States | 1 |
| 3 | 44 | Private | 10 | Married-civ-spouse | Machine-op-inspct | Husband | Black | 1 | 7688 | 0 | 40 | United-States | 1 |
| 4 | 18 | NaN | 10 | Never-married | NaN | Own-child | White | 0 | 0 | 0 | 30 | United-States | 0 |
occupation¶
In [21]:
df['occupation'].value_counts()
Out[21]:
Prof-specialty 6172 Craft-repair 6112 Exec-managerial 6086 Adm-clerical 5611 Sales 5504 Other-service 4923 Machine-op-inspct 3022 Transport-moving 2355 Handlers-cleaners 2072 Farming-fishing 1490 Tech-support 1446 Protective-serv 983 Priv-house-serv 242 Armed-Forces 15 Name: occupation, dtype: int64
occupation: data 이미 category화 돼있음, object와 종속변수의 관계성 높아 보임 > dummies화
native-country¶
In [22]:
df['native-country'].value_counts()
Out[22]:
United-States 43832 Mexico 951 Philippines 295 Germany 206 Puerto-Rico 184 Canada 182 El-Salvador 155 India 151 Cuba 138 England 127 China 122 South 115 Jamaica 106 Italy 105 Dominican-Republic 103 Japan 92 Guatemala 88 Poland 87 Vietnam 86 Columbia 85 Haiti 75 Portugal 67 Taiwan 65 Iran 59 Greece 49 Nicaragua 49 Peru 46 Ecuador 45 France 38 Ireland 37 Hong 30 Thailand 30 Cambodia 28 Trinadad&Tobago 27 Yugoslavia 23 Outlying-US(Guam-USVI-etc) 23 Laos 23 Scotland 21 Honduras 20 Hungary 19 Holand-Netherlands 1 Name: native-country, dtype: int64
native-countrydata 처리
United-Statesvs 나머지: 0, 1대륙권, 언어권별: 추가 기준을 만들어서 더미변수화
다 1, 2, 3, 4로 순서를 지정: 고연종바 비율(class 비율)
In [23]:
# 'native-country' column 기준 조회: df.groupby('native-country')['적용 coulmn'].집계함수()
df.groupby('native-country')['class'].mean().sort_values(ascending=False)
Out[23]:
native-country France 0.421053 India 0.410596 Taiwan 0.400000 Iran 0.372881 England 0.370079 Greece 0.367347 Yugoslavia 0.347826 Japan 0.347826 Canada 0.346154 Italy 0.323810 Cambodia 0.321429 Hungary 0.315789 Ireland 0.297297 China 0.295082 Philippines 0.288136 Germany 0.281553 Hong 0.266667 Cuba 0.246377 United-States 0.243977 Poland 0.195402 Portugal 0.179104 South 0.173913 Thailand 0.166667 Scotland 0.142857 Jamaica 0.141509 Ecuador 0.133333 Haiti 0.120000 Puerto-Rico 0.108696 Honduras 0.100000 Peru 0.086957 Laos 0.086957 Vietnam 0.081395 Trinadad&Tobago 0.074074 El-Salvador 0.070968 Nicaragua 0.061224 Mexico 0.049422 Dominican-Republic 0.048544 Columbia 0.047059 Outlying-US(Guam-USVI-etc) 0.043478 Guatemala 0.034091 Holand-Netherlands 0.000000 Name: class, dtype: float64
- 1위 평균소득 국가 'France' Data 분석
In [24]:
df[df['native-country'] == 'France'].groupby('occupation')['class'].mean()
Out[24]:
occupation Adm-clerical 0.666667 Craft-repair 0.000000 Exec-managerial 0.555556 Farming-fishing 0.000000 Other-service 0.000000 Priv-house-serv 0.000000 Prof-specialty 0.272727 Protective-serv 1.000000 Sales 1.000000 Tech-support 0.750000 Name: class, dtype: float64
In [25]:
df[df['native-country'] == 'France'].groupby('occupation')['class'].count()
Out[25]:
occupation Adm-clerical 3 Craft-repair 1 Exec-managerial 9 Farming-fishing 1 Other-service 2 Priv-house-serv 2 Prof-specialty 11 Protective-serv 1 Sales 2 Tech-support 4 Name: class, dtype: int64
native-country별class의 평균을Series로 추출 >country_group
In [26]:
country_group = df.groupby('native-country')['class'].mean()
country_group
Out[26]:
native-country Cambodia 0.321429 Canada 0.346154 China 0.295082 Columbia 0.047059 Cuba 0.246377 Dominican-Republic 0.048544 Ecuador 0.133333 El-Salvador 0.070968 England 0.370079 France 0.421053 Germany 0.281553 Greece 0.367347 Guatemala 0.034091 Haiti 0.120000 Holand-Netherlands 0.000000 Honduras 0.100000 Hong 0.266667 Hungary 0.315789 India 0.410596 Iran 0.372881 Ireland 0.297297 Italy 0.323810 Jamaica 0.141509 Japan 0.347826 Laos 0.086957 Mexico 0.049422 Nicaragua 0.061224 Outlying-US(Guam-USVI-etc) 0.043478 Peru 0.086957 Philippines 0.288136 Poland 0.195402 Portugal 0.179104 Puerto-Rico 0.108696 Scotland 0.142857 South 0.173913 Taiwan 0.400000 Thailand 0.166667 Trinadad&Tobago 0.074074 United-States 0.243977 Vietnam 0.081395 Yugoslavia 0.347826 Name: class, dtype: float64
In [27]:
country_group.index
Out[27]:
Index(['Cambodia', 'Canada', 'China', 'Columbia', 'Cuba', 'Dominican-Republic',
'Ecuador', 'El-Salvador', 'England', 'France', 'Germany', 'Greece',
'Guatemala', 'Haiti', 'Holand-Netherlands', 'Honduras', 'Hong',
'Hungary', 'India', 'Iran', 'Ireland', 'Italy', 'Jamaica', 'Japan',
'Laos', 'Mexico', 'Nicaragua', 'Outlying-US(Guam-USVI-etc)', 'Peru',
'Philippines', 'Poland', 'Portugal', 'Puerto-Rico', 'Scotland', 'South',
'Taiwan', 'Thailand', 'Trinadad&Tobago', 'United-States', 'Vietnam',
'Yugoslavia'],
dtype='object', name='native-country')
In [28]:
country_group = country_group.reset_index()
country_group
Out[28]:
| native-country | class | |
|---|---|---|
| 0 | Cambodia | 0.321429 |
| 1 | Canada | 0.346154 |
| 2 | China | 0.295082 |
| 3 | Columbia | 0.047059 |
| 4 | Cuba | 0.246377 |
| 5 | Dominican-Republic | 0.048544 |
| 6 | Ecuador | 0.133333 |
| 7 | El-Salvador | 0.070968 |
| 8 | England | 0.370079 |
| 9 | France | 0.421053 |
| 10 | Germany | 0.281553 |
| 11 | Greece | 0.367347 |
| 12 | Guatemala | 0.034091 |
| 13 | Haiti | 0.120000 |
| 14 | Holand-Netherlands | 0.000000 |
| 15 | Honduras | 0.100000 |
| 16 | Hong | 0.266667 |
| 17 | Hungary | 0.315789 |
| 18 | India | 0.410596 |
| 19 | Iran | 0.372881 |
| 20 | Ireland | 0.297297 |
| 21 | Italy | 0.323810 |
| 22 | Jamaica | 0.141509 |
| 23 | Japan | 0.347826 |
| 24 | Laos | 0.086957 |
| 25 | Mexico | 0.049422 |
| 26 | Nicaragua | 0.061224 |
| 27 | Outlying-US(Guam-USVI-etc) | 0.043478 |
| 28 | Peru | 0.086957 |
| 29 | Philippines | 0.288136 |
| 30 | Poland | 0.195402 |
| 31 | Portugal | 0.179104 |
| 32 | Puerto-Rico | 0.108696 |
| 33 | Scotland | 0.142857 |
| 34 | South | 0.173913 |
| 35 | Taiwan | 0.400000 |
| 36 | Thailand | 0.166667 |
| 37 | Trinadad&Tobago | 0.074074 |
| 38 | United-States | 0.243977 |
| 39 | Vietnam | 0.081395 |
| 40 | Yugoslavia | 0.347826 |
country_groupSeries를 원본DataFrame에 병합(merge)- 기준 key:
country_group, 기준 idx:native-country:df.merge(country_group, on='native-country')
- 기준 key:
A.merge(B):left_obj = A, right_obj = Bleft_obj를 기준,right_obj의 결측치를 무시:how='left'
In [29]:
df = df.merge(country_group, on='native-country', how='left')
In [30]:
df.head()
Out[30]:
| age | workclass | education-num | marital-status | occupation | relationship | race | sex | capital-gain | capital-loss | hours-per-week | native-country | class_x | class_y | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 25 | Private | 7 | Never-married | Machine-op-inspct | Own-child | Black | 1 | 0 | 0 | 40 | United-States | 0 | 0.243977 |
| 1 | 38 | Private | 9 | Married-civ-spouse | Farming-fishing | Husband | White | 1 | 0 | 0 | 50 | United-States | 0 | 0.243977 |
| 2 | 28 | Local-gov | 12 | Married-civ-spouse | Protective-serv | Husband | White | 1 | 0 | 0 | 40 | United-States | 1 | 0.243977 |
| 3 | 44 | Private | 10 | Married-civ-spouse | Machine-op-inspct | Husband | Black | 1 | 7688 | 0 | 40 | United-States | 1 | 0.243977 |
| 4 | 18 | NaN | 10 | Never-married | NaN | Own-child | White | 0 | 0 | 0 | 30 | United-States | 0 | 0.243977 |
In [31]:
df.drop('native-country', axis=1, inplace=True)
df.head()
Out[31]:
| age | workclass | education-num | marital-status | occupation | relationship | race | sex | capital-gain | capital-loss | hours-per-week | class_x | class_y | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 25 | Private | 7 | Never-married | Machine-op-inspct | Own-child | Black | 1 | 0 | 0 | 40 | 0 | 0.243977 |
| 1 | 38 | Private | 9 | Married-civ-spouse | Farming-fishing | Husband | White | 1 | 0 | 0 | 50 | 0 | 0.243977 |
| 2 | 28 | Local-gov | 12 | Married-civ-spouse | Protective-serv | Husband | White | 1 | 0 | 0 | 40 | 1 | 0.243977 |
| 3 | 44 | Private | 10 | Married-civ-spouse | Machine-op-inspct | Husband | Black | 1 | 7688 | 0 | 40 | 1 | 0.243977 |
| 4 | 18 | NaN | 10 | Never-married | NaN | Own-child | White | 0 | 0 | 0 | 30 | 0 | 0.243977 |
In [32]:
df = df.rename(columns={'class_x':'class', 'class_y':'native-country'})
df.head()
Out[32]:
| age | workclass | education-num | marital-status | occupation | relationship | race | sex | capital-gain | capital-loss | hours-per-week | class | native-country | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 25 | Private | 7 | Never-married | Machine-op-inspct | Own-child | Black | 1 | 0 | 0 | 40 | 0 | 0.243977 |
| 1 | 38 | Private | 9 | Married-civ-spouse | Farming-fishing | Husband | White | 1 | 0 | 0 | 50 | 0 | 0.243977 |
| 2 | 28 | Local-gov | 12 | Married-civ-spouse | Protective-serv | Husband | White | 1 | 0 | 0 | 40 | 1 | 0.243977 |
| 3 | 44 | Private | 10 | Married-civ-spouse | Machine-op-inspct | Husband | Black | 1 | 7688 | 0 | 40 | 1 | 0.243977 |
| 4 | 18 | NaN | 10 | Never-married | NaN | Own-child | White | 0 | 0 | 0 | 30 | 0 | 0.243977 |
결측치 처리¶
In [33]:
df.isnull().mean()
Out[33]:
age 0.000000 workclass 0.057307 education-num 0.000000 marital-status 0.000000 occupation 0.057512 relationship 0.000000 race 0.000000 sex 0.000000 capital-gain 0.000000 capital-loss 0.000000 hours-per-week 0.000000 class 0.000000 native-country 0.017546 dtype: float64
native-country: 임의의 값 넣기¶
In [34]:
df['native-country'].fillna(0, inplace=True)
df.isna().mean()
Out[34]:
age 0.000000 workclass 0.057307 education-num 0.000000 marital-status 0.000000 occupation 0.057512 relationship 0.000000 race 0.000000 sex 0.000000 capital-gain 0.000000 capital-loss 0.000000 hours-per-week 0.000000 class 0.000000 native-country 0.000000 dtype: float64
workclass¶
- Data별 비율 확인
In [35]:
df['workclass'].value_counts() / len(df)
Out[35]:
Private 0.694198 Self-emp-not-inc 0.079071 Local-gov 0.064207 State-gov 0.040559 Self-emp-inc 0.034704 Federal-gov 0.029319 Without-pay 0.000430 Never-worked 0.000205 Name: workclass, dtype: float64
- 최빈값으로 채우기
In [36]:
df['workclass'].fillna('Private', inplace=True)
df['workclass'].isnull().sum()
Out[36]:
0
In [37]:
df.isna().mean()
Out[37]:
age 0.000000 workclass 0.000000 education-num 0.000000 marital-status 0.000000 occupation 0.057512 relationship 0.000000 race 0.000000 sex 0.000000 capital-gain 0.000000 capital-loss 0.000000 hours-per-week 0.000000 class 0.000000 native-country 0.000000 dtype: float64
occupation¶
In [38]:
df['occupation'].value_counts()
Out[38]:
Prof-specialty 6172 Craft-repair 6112 Exec-managerial 6086 Adm-clerical 5611 Sales 5504 Other-service 4923 Machine-op-inspct 3022 Transport-moving 2355 Handlers-cleaners 2072 Farming-fishing 1490 Tech-support 1446 Protective-serv 983 Priv-house-serv 242 Armed-Forces 15 Name: occupation, dtype: int64
더미변수화¶
In [39]:
df = pd.get_dummies(df, drop_first=True)
df.info()
<class 'pandas.core.frame.DataFrame'> Int64Index: 48842 entries, 0 to 48841 Data columns (total 43 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 age 48842 non-null int64 1 education-num 48842 non-null int64 2 sex 48842 non-null uint8 3 capital-gain 48842 non-null int64 4 capital-loss 48842 non-null int64 5 hours-per-week 48842 non-null int64 6 class 48842 non-null int64 7 native-country 48842 non-null float64 8 workclass_Local-gov 48842 non-null uint8 9 workclass_Never-worked 48842 non-null uint8 10 workclass_Private 48842 non-null uint8 11 workclass_Self-emp-inc 48842 non-null uint8 12 workclass_Self-emp-not-inc 48842 non-null uint8 13 workclass_State-gov 48842 non-null uint8 14 workclass_Without-pay 48842 non-null uint8 15 marital-status_Married-AF-spouse 48842 non-null uint8 16 marital-status_Married-civ-spouse 48842 non-null uint8 17 marital-status_Married-spouse-absent 48842 non-null uint8 18 marital-status_Never-married 48842 non-null uint8 19 marital-status_Separated 48842 non-null uint8 20 marital-status_Widowed 48842 non-null uint8 21 occupation_Armed-Forces 48842 non-null uint8 22 occupation_Craft-repair 48842 non-null uint8 23 occupation_Exec-managerial 48842 non-null uint8 24 occupation_Farming-fishing 48842 non-null uint8 25 occupation_Handlers-cleaners 48842 non-null uint8 26 occupation_Machine-op-inspct 48842 non-null uint8 27 occupation_Other-service 48842 non-null uint8 28 occupation_Priv-house-serv 48842 non-null uint8 29 occupation_Prof-specialty 48842 non-null uint8 30 occupation_Protective-serv 48842 non-null uint8 31 occupation_Sales 48842 non-null uint8 32 occupation_Tech-support 48842 non-null uint8 33 occupation_Transport-moving 48842 non-null uint8 34 relationship_Not-in-family 48842 non-null uint8 35 relationship_Other-relative 48842 non-null uint8 36 relationship_Own-child 48842 non-null uint8 37 relationship_Unmarried 48842 non-null uint8 38 relationship_Wife 48842 non-null uint8 39 race_Asian-Pac-Islander 48842 non-null uint8 40 race_Black 48842 non-null uint8 41 race_Other 48842 non-null uint8 42 race_White 48842 non-null uint8 dtypes: float64(1), int64(6), uint8(36) memory usage: 4.7 MB
Training Model¶
In [40]:
from sklearn.model_selection import train_test_split
X = df.drop('class', axis=1)
y = df['class']
X_train, X_test, y_train, y_test = train_test_split(
X, y, test_size=0.3, random_state=814
)
In [41]:
from sklearn.tree import DecisionTreeClassifier
model = DecisionTreeClassifier(random_state=814)
model.fit(X_train, y_train)
pred = model.predict(X_test)
pred
Out[41]:
array([0, 0, 0, ..., 1, 0, 0])
Evaluating Model¶
In [42]:
from sklearn.metrics import accuracy_score
accuracy_score(y_test, pred)
Out[42]:
0.8173752815123183
Hyperparameter tuning¶
In [43]:
X_train, X_test, y_train, y_test = train_test_split(
X, y, test_size=0.8, random_state=814
)
model = DecisionTreeClassifier(random_state=100)
model.fit(X_train, y_train)
train_pred = model.predict(X_train)
test_pred = model.predict(X_test)
print(f'Train score: {accuracy_score(y_train, train_pred)}')
print(f'Test score: {accuracy_score(y_test, test_pred)}')
Train score: 0.9880221130221131 Test score: 0.8129702615549982
train set 비중이 많을 때: train set의 학습 결과 과최적화 (Overfitting)
- test set에서의 정확도는 오히려 떨어진다
In [44]:
model = DecisionTreeClassifier(max_depth=5, random_state=814)
model.fit(X_train, y_train)
train_pred = model.predict(X_train)
test_pred = model.predict(X_test)
print('Train score:', accuracy_score(y_train, train_pred))
print('Test score:', accuracy_score(y_test, test_pred))
Train score: 0.8536036036036037 Test score: 0.8465219839279317
max_depth=5깊이 제한- fitting 수준 하락, 오히려 새로운 데이터
y_test에 대한 예측력 상승
- fitting 수준 하락, 오히려 새로운 데이터
tree depth가 깊어지면 overfitting이 발생한다¶
결정 트리의 기본값 설정은 구분되는 것까지 가장 깊은 Node까지 탐색
Overfitting(과최적화): 과도한 학습(fitting)으로 인해 새로운 data에 대한 예측력이 오히려 떨어지는 상황
In [45]:
def tuning_depth(depth: int):
model = DecisionTreeClassifier(max_depth=depth, random_state=814)
model.fit(X_train, y_train)
train_pred = model.predict(X_train)
test_pred = model.predict(X_test)
print('depth=', depth)
print('Train score:', accuracy_score(y_train, train_pred))
print('Test score:', accuracy_score(y_test, test_pred))
In [46]:
for i in range(1, 21):
tuning_depth(i)
depth= 1 Train score: 0.7619778869778869 Test score: 0.7604033372575114 depth= 2 Train score: 0.8293407043407044 Test score: 0.8289655525413319 depth= 3 Train score: 0.8455159705159705 Test score: 0.8438091825766494 depth= 4 Train score: 0.8471539721539721 Test score: 0.8434252955929774 depth= 5 Train score: 0.8536036036036037 Test score: 0.8465219839279317 depth= 6 Train score: 0.8610769860769861 Test score: 0.8517428469058709 depth= 7 Train score: 0.8645577395577395 Test score: 0.8512565900598864 depth= 8 Train score: 0.871007371007371 Test score: 0.8473153503608538 depth= 9 Train score: 0.8769451269451269 Test score: 0.8480575318626197 depth= 10 Train score: 0.8833947583947583 Test score: 0.8500537441777141 depth= 11 Train score: 0.8908681408681409 Test score: 0.8482110866560885 depth= 12 Train score: 0.8990581490581491 Test score: 0.8442698469570559 depth= 13 Train score: 0.9057125307125307 Test score: 0.8409172339663203 depth= 14 Train score: 0.9129811629811629 Test score: 0.8405845319138046 depth= 15 Train score: 0.9206592956592956 Test score: 0.8295029943184726 depth= 16 Train score: 0.9297706797706797 Test score: 0.8263295285867841 depth= 17 Train score: 0.937039312039312 Test score: 0.8249475354455649 depth= 18 Train score: 0.944000819000819 Test score: 0.8246148333930491 depth= 19 Train score: 0.949017199017199 Test score: 0.8229257306648923 depth= 20 Train score: 0.9543407043407044 Test score: 0.8177560526181092
트리 그래프¶
In [47]:
from sklearn.tree import plot_tree
# 그래프 크기 설정
plt.figure(figsize=(30, 15))
plot_tree(model)
plt.show()
In [48]:
plt.figure(figsize=(30, 15))
plot_tree(model, max_depth=3, fontsize=15)
plt.show()
In [49]:
plt.figure(figsize=(30,15))
plot_tree(model, max_depth = 3, fontsize=15, feature_names=X_train.columns)
plt.show()
In [50]:
!pip install mlxtend --quiet
In [51]:
import joblib
model = DecisionTreeClassifier(max_depth=8)
model.fit(X_train, y_train)
joblib.dump(model, 'DecisionTree_model.pkl')
Out[51]:
['DecisionTree_model.pkl']
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