from pandas import read_csv
from sklearn.model_selection import kfold
from sklearn.model_selection import cross_val_score
from sklearn.ensemble import baggingclassifier
from sklearn.tree import decisiontreeclassifier#袋裝演算法
from sklearn.ensemble import randomforestclassifier#隨機森林
from sklearn.ensemble import extratreesclassifier#極端隨機樹
from sklearn.ensemble import adaboostclassifier#adaboost,迭代演算法
from sklearn.ensemble import gradientboostingclassifier#隨機梯度提公升(gbm)
# 匯入資料
filename = 'd:\example\machinelearning-master\pima_data.csv'
names = ['preg', 'plas', 'pres', 'skin', 'test', 'mass', 'pedi', 'age', 'class']
data = read_csv(filename, names=names)
# 將資料分為輸入資料和輸出結果
array = data.values
x = array[:, 0:8]
y = array[:, 8]
num_folds = 10
seed = 7
kfold = kfold(n_splits=num_folds, random_state=seed)
cart = decisiontreeclassifier()
num_tree = 100
model = baggingclassifier(base_estimator=cart, n_estimators=num_tree, random_state=seed)#袋裝演算法
result = cross_val_score(model, x, y, cv=kfold)
print('袋裝演算法:',result.mean())
kfold = kfold(n_splits=num_folds, random_state=seed)
num_tree = 100
max_features = 3
model = randomforestclassifier(n_estimators=num_tree, random_state=seed, max_features=max_features)#隨機森林
result = cross_val_score(model, x, y, cv=kfold)
print('隨機森林:',result.mean())
kfold = kfold(n_splits=num_folds, random_state=seed)
num_tree = 100
max_features = 3
model = extratreesclassifier(n_estimators=num_tree, random_state=seed, max_features=max_features)# 極端隨機樹
result = cross_val_score(model, x, y, cv=kfold)
print('極端隨機樹:',result.mean())
kfold = kfold(n_splits=num_folds, random_state=seed)
num_tree = 30
model = adaboostclassifier(n_estimators=num_tree, random_state=seed)
result = cross_val_score(model, x, y, cv=kfold)#adaboost,迭代演算法
print('adaboost,迭代演算法:',result.mean())
kfold = kfold(n_splits=num_folds, random_state=seed)
num_tree = 30
model = gradientboostingclassifier(n_estimators=num_tree, random_state=seed)
result = cross_val_score(model, x, y, cv=kfold)#隨機梯度提公升
print('隨機梯度提公升:',result.mean())
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