[XGBoost] 위스콘신 유방암 데이터 (1)

위스콘신 유방암 데이터

 

사이킷런에서는 UCI ML 유방암 위스콘신 (진단) 데이터 세트를 제공한다. 또한, 사본도 다운로드 가능하다.

 

라이브러리 설치

 

conda install -c conda-forge xgboost
conda install -c conda-forge imbalanced-learn

 

breast-cancer-wisconsin 데이터셋 로드

 

breast-cancer-wisconsin.data.csv

0.02MB

import pandas as pd

df = pd.read_csv('breast-cancer-wisconsin.data.csv', names=['id','Clump Thickness','Uniformity of Cell Size','Uniformity of Cell Shape','Marginal Adhesion','Single Epithelial Cell Size','Bare Nuclei','Bland Chromatin','Normal Nucleoli','Mitoses','Outcome'])
df.head()

# Outcome 값 변경
df.loc[df['Outcome']==2,'Outcome'] = 0
df.loc[df['Outcome']==4,'Outcome'] = 1

df.describe()

# 데이터프레임 데이터값 확인
df.dtypes

# 결측치 제거 및 Outcome 비율 확인
for key in df.keys():
    df.loc[df[key]=='?', key] = None
    
df.dropna(inplace=True)
df.reset_index(inplace=True, drop=True)

df['Outcome'].value_counts()

0.0    444
1.0    239
Name: Outcome, dtype: int64

 

데이터 전처리

 

# 훈련 데이터와 테스트 데이터 나누기
features = df[df.keys().drop(['id','Outcome'])].values
outcome = df['Outcome'].values.reshape(-1,1)

from sklearn.model_selection import train_test_split

train_features, test_features, train_target, test_target = train_test_split(features, outcome, stratify=outcome, test_size=0.3)

 

데이터 스케일링 수행

 

from sklearn.preprocessing import MinMaxScaler

feature_scaler = MinMaxScaler()
train_features_scaled = feature_scaler.fit_transform(train_features)
test_features_scaled = feature_scaler.transform(test_features)

 

XGBClassifier로 학습

 

from xgboost import XGBClassifier

xgb = XGBClassifier()
xgb.fit(train_features_scaled, train_target)
result = xgb.predict(test_features_scaled)

from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score

print('Accuracy :',accuracy_score(test_target, result))
print('Precision :',precision_score(test_target, result))
print('Recall :',recall_score(test_target, result))
print('F1 score :',f1_score(test_target, result))

Accuracy : 0.9707317073170731
Precision : 0.9342105263157895
Recall : 0.9861111111111112
F1 score : 0.9594594594594595