ml_performance_model.py

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

path = r"D:\computer_vision\data\diabetes_1.csv"
headernames = ['preg', 'plas', 'pres', 'skin', 'test', 'mass', 'pedi', 'age', 'class']
data = read_csv(path, names=headernames)
array = data.values
X = array[:,0:8]
Y = array[:,8]
seed = 7
kfold = KFold(n_splits=10, random_state=seed)
cart = DecisionTreeClassifier()
num_trees = 150
model = BaggingClassifier(base_estimator=cart, n_estimators=num_trees, random_state=seed)
results = cross_val_score(model, X, Y, cv=kfold)
print(results.mean())


# adda boost
from pandas import read_csv
from sklearn.model_selection import KFold
from sklearn.model_selection import cross_val_score
from sklearn.ensemble import AdaBoostClassifier

path  = r"D:\computer_vision\data\diabetes_1.csv"
headernames = ['preg', 'plas', 'pres', 'skin', 'test', 'mass', 'pedi', 'age', 'class']
data = read_csv(path, names=headernames)
array = data.values
X = array[:,0:8]
Y = array[:,8]
seed = 5
kfold = KFold(n_splits=10, random_state=seed)
num_trees = 50
model = AdaBoostClassifier(n_estimators=num_trees, random_state=seed)
results = cross_val_score(model, X, Y, cv=kfold)
print(results.mean())

#sochastic gradient
from pandas import read_csv
from sklearn.model_selection import KFold
from sklearn.model_selection import cross_val_score
from sklearn.ensemble import GradientBoostingClassifier


path = r"D:\computer_vision\data\diabetes_1.csv"
headernames = ['preg', 'plas', 'pres', 'skin', 'test', 'mass', 'pedi', 'age', 'class']
data = read_csv(path, names=headernames)
array = data.values
X = array[:,0:8]
Y = array[:,8]

seed = 5
kfold = KFold(n_splits=10, random_state=seed)
num_trees = 50
model = GradientBoostingClassifier(n_estimators=num_trees, random_state=seed)
results = cross_val_score(model, X, Y, cv=kfold)
print(results.mean())


#voting classifier
from pandas import read_csv
from sklearn.model_selection import KFold
from sklearn.model_selection import cross_val_score
from sklearn.linear_model import LogisticRegression
from sklearn.tree import DecisionTreeClassifier
from sklearn.svm import SVC
from sklearn.ensemble import VotingClassifier

path = r"D:\computer_vision\data\diabetes_1.csv"
headernames = ['preg', 'plas', 'pres', 'skin', 'test', 'mass', 'pedi', 'age', 'class']
data = read_csv(path, names=headernames)
array = data.values
X = array[:,0:8]
Y = array[:,8]

kfold = KFold(n_splits=10, random_state=7)
estimators = []
model1 = LogisticRegression()
estimators.append(('logistic', model1))
model2 = DecisionTreeClassifier()
estimators.append(('cart', model2))
model3 = SVC()
estimators.append(('svm', model3))

ensemble = VotingClassifier(estimators)
results = cross_val_score(ensemble, X, Y, cv=kfold)
print(results.mean())