LR/SVM/DT/AdaBoost/NB 糖尿病预测 （2）

各类算法 预测糖尿病：

见我原文：KNeighbors 糖尿病预测

import matplotlib.pyplot as plt
import numpy as np
import pandas as pd

# 加载数据
data = pd.read_csv(r'C:\Users\Qiuyi\Desktop\scikit-learn code\code\datasets\pima-indians-diabetes\diabetes.csv')
print('dataset shape {}'.format(data.shape))
data.head()

dataset shape (768, 9)

将特征和标签（列）分离:

X = data.iloc[:, 0:8]
Y = data.iloc[:, 8]
print('shape of X {}; shape of Y {}'.format(X.shape, Y.shape))

shape of X (768, 8); shape of Y (768,)

将数据及分成训练集和测试集：

from sklearn.model_selection import train_test_split
X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.2)

逻辑回归算法：

sklearn.linear_model.LinearRegression

支持向量机：

sklearn.svm.SVC

决策树：

sklearn.tree.DecisionTreeClassifier

AdaBoost（自适应增强）：

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sklearn.ensemble.AdaBoostClassifier

朴素贝叶斯：

sklearn.naive_bayes.GaussianNB

from sklearn.neighbors import KNeighborsClassifier
from sklearn.linear_model import LinearRegression
from sklearn.svm import SVC
from sklearn.tree import DecisionTreeClassifier
from sklearn.ensemble import AdaBoostClassifier
from sklearn.naive_bayes import MultinomialNB

models = []
models.append(("KNN", KNeighborsClassifier(n_neighbors=2)))
models.append(("LR",LinearRegression()))
models.append(("SVC",SVC()))
models.append(("DTC",DecisionTreeClassifier()))
models.append(("AdaBoost",AdaBoostClassifier()))
models.append(("GNB",MultinomialNB()))

from sklearn.model_selection import KFold
from sklearn.model_selection import cross_val_score

results = []
for name, model in models:
    kfold = KFold(n_splits=10)
    cv_result = cross_val_score(model, X, Y, cv=kfold)
    results.append((name, cv_result))
for i in range(len(results)):
    print("name: {}; cross val score: {}".format(
        results[i][0],results[i][1].mean()))

name: KNN; cross val score: 0.7147641831852358
name: LR; cross val score: 0.2580299922161077
name: SVC; cross val score: 0.6510252904989747
name: DTC; cross val score: 0.6951982228298018
name: AdaBoost; cross val score: 0.7539473684210527
name: GNB; cross val score: 0.5909603554340397

现在还不会调，以后再改进。