[ML] Wine Dataset - Decision Tree

박무연·2023년 3월 17일

머신러닝 제로베이스 데이터취업스쿨 파이썬

🟤Mahine Learning

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5/13

Decsion Tree (Classfication)

데이터 전처리

- 데이터 합치기

red_wine['color'] = 1
white_wine['color'] = 0
# red : 1 ,white : 0

wine = pd.concat([red_wine,white_wine])

redwine 데이터와 whitewine 데이터 합치기기

- quality histogram

import plotly.express as px

fig = px.histogram(wine, x='quality')
fig.show()

quality 별 히스토그램 작성

- 와인종류별 퀄리티별 히스토그램

fig = px.histogram(wine , x='quality', color='color')
fig.show()

하나의 컬럼에 여러개의 value를 가지고 있다면 color로 그래프를 다르게 그릴 수 있다

레드 와인/ 화이트 와인 분류기

- 데이터 split

X = wine.drop(['color'], axis=1)
y = wine['color']



from sklearn.model_selection import train_test_split
import numpy as np

X_train, X_test , y_train, y_test = train_test_split(X, y, 
				test_size=0.2,                           random_state = 13)

- Train, Test 구경하기

import plotly.graph_objects as go

fig =go.Figure()
fig.add_trace(go.Histogram(x=X_train['quality'], name ='Train'))
fig.add_trace(go.Histogram(x=X_test['quality'], name ='Test'))


fig.update_layout(barmode='overlay')
fig.update_traces(opacity=0.75)
fig.show()

update_layout : 그래프를 어떻게 나타낼 것인가 Stack, overlay, 등등..
(참고 : https://plotly.com/python/reference/layout/)
Train 과 Test 셋에 비슷한 비율로 데이터가 분리되어 있다

- 학습 및 평가

from sklearn.tree import DecisionTreeClassifier

clf = DecisionTreeClassifier(max_depth=2 , random_state=13)
clf.fit(X_train, y_train)

from sklearn.metrics import accuracy_score

y_pred_tr = clf.predict(X_train)
y_pred_test = clf.predict(X_test)

print('Train ACC : ', accuracy_score(y_train,y_pred_tr))
print('Test ACC : ', accuracy_score(y_test,y_pred_test))

>>>
Train ACC :  0.9553588608812776
Test ACC :  0.9569230769230769

데이터 전처기

- 데이터 들여다 보기

fig = go.Figure()
fig.add_trace(go.Box(y=X['fixed acidity'], name ='fixed acidity'))
fig.add_trace(go.Box(y=X['chlorides'], name ='chlorides'))
fig.add_trace(go.Box(y=X['quality'], name ='quality'))

fig.show()

차이가 너무 크다
특성의 편향 문제는 최적의 모델을 찾는데 방해가 될 수 있다
전처리를 통해 줄여보자
Decision Tree는 크게 의미 없긴하다
어떤 전처리를 사용할 것인지는 해봐야 안다!

- 데이터 전처리

from sklearn.preprocessing import MinMaxScaler, StandardScaler

mms = MinMaxScaler()
ss = StandardScaler()

X_ss = ss.fit_transform(X)
X_mms = mms.fit_transform(X)

X_ss_pd = pd.DataFrame(X_ss, columns=X.columns)
X_mms_pd = pd.DataFrame(X_mms, columns=X.columns)

StandardScaler , Min-MaxScaler 모두 해본다

- 학습 및 평가

Standard Scaler

X_train, X_test , y_train, y_test = train_test_split(X_mms_pd, y, test_size=0.2,
                                                     random_state = 13)


clf = DecisionTreeClassifier(max_depth=2 , random_state=13)
clf.fit(X_train, y_train)

y_pred_tr = clf.predict(X_train)
y_pred_test = clf.predict(X_test)

print('Train ACC : ', accuracy_score(y_train,y_pred_tr))
print('Test ACC : ', accuracy_score(y_test,y_pred_test))

>>>
Train ACC :  0.9553588608812776
Test ACC :  0.9569230769230769

Min-Max Scaler

X_train, X_test , y_train, y_test = train_test_split(X_ss_pd, y, test_size=0.2,
                                                     random_state = 13)


clf = DecisionTreeClassifier(max_depth=2 , random_state=13)
clf.fit(X_train, y_train)

y_pred_tr = clf.predict(X_train)
y_pred_test = clf.predict(X_test)

print('Train ACC : ', accuracy_score(y_train,y_pred_tr))
print('Test ACC : ', accuracy_score(y_test,y_pred_test))

>>>
Train ACC :  0.9553588608812776
Test ACC :  0.9569230769230769

중요 특성 보기

dict(zip(X_train.columns, clf.feature_importances_))

>>>
{'fixed acidity': 0.0,
 'volatile acidity': 0.0,
 'citric acid': 0.0,
 'residual sugar': 0.0,
 'chlorides': 0.24230360549660776,
 'free sulfur dioxide': 0.0,
 'total sulfur dioxide': 0.7576963945033922,
 'density': 0.0,
 'pH': 0.0,
 'sulphates': 0.0,
 'alcohol': 0.0,
 'quality': 0.0}

와인 맛에 대한 분류 - 이진 분류

- 이진화 (quality 컬럼 제외)

wine['taste'] = [1 if grade > 5 else 0 for grade in wine['quality']]

- 학습 및 평과

X = wine.drop(['taste','quality'], axis=1)
y = wine['taste']

X_train, X_test ,y_train, y_test = train_test_split(X,y, test_size = 0.2,
                                                    random_state = 13)

clf = DecisionTreeClassifier(max_depth=2, random_state=13)
clf.fit(X_train, y_train)

y_pred_train = clf.predict(X_train)
y_pred_test = clf.predict(X_test)

print(f'Train ACC : {accuracy_score(y_train, y_pred_train)}')
print(f'test ACC : {accuracy_score(y_test, y_pred_test)}')

>>>
Train ACC : 0.7294593034442948
test ACC : 0.7161538461538461

- Tree 시각화

import matplotlib.pyplot as plt
import sklearn.tree as tree

plt.figure(figsize=(12,8))
tree.plot_tree(clf, feature_names=X.columns,
                rounded=True,
               filled=True);