ML之分类预测：以六类机器学习算法(kNN、逻辑回归、SVM、决策树、随机森林、提升树、神经网络)对糖尿病数据集(8→1)实现二分类模型评估案例来理解和认知机器学习分类预测的模板流程
目录
六类机器学习算法(kNN、逻辑回归、SVM、决策树、随机森林、提升树、神经网络)对糖尿病数据集(8→1)实现二分类预测
相关文章
ML之分类预测：以六类机器学习算法(kNN、逻辑回归、SVM、决策树、随机森林、提升树、神经网络)对糖尿病数据集(8→1)实现二分类模型评估案例来理解和认知机器学习分类预测
ML之分类预测：以六类机器学习算法(kNN、逻辑回归、SVM、决策树、随机森林、提升树、神经网络)对糖尿病数据集(8→1)实现二分类模型评估案例来理解和认知机器学习分类预测应用
六类机器学习算法(kNN、逻辑回归、SVM、决策树、随机森林、提升树、神经网络)对糖尿病数据集(8→1)实现二分类预测
数据集理解
data.shape: (768, 9)
data.columns：
Index(['Pregnancies', 'Glucose', 'BloodPressure', 'SkinThickness', 'Insulin',
'BMI', 'DiabetesPedigreeFunction', 'Age', 'Outcome'],
dtype='object')
data.head：
Pregnancies Glucose BloodPressure ... DiabetesPedigreeFunction Age Outcome
0 6 148 72 ... 0.627 50 1
1 1 85 66 ... 0.351 31 0
2 8 183 64 ... 0.672 32 1
3 1 89 66 ... 0.167 21 0
4 0 137 40 ... 2.288 33 1
[5 rows x 9 columns]
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 768 entries, 0 to 767
Data columns (total 9 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 Pregnancies 768 non-null int64
1 Glucose 768 non-null int64
2 BloodPressure 768 non-null int64
3 SkinThickness 768 non-null int64
4 Insulin 768 non-null int64
5 BMI 768 non-null float64
6 DiabetesPedigreeFunction 768 non-null float64
7 Age 768 non-null int64
8 Outcome 768 non-null int64
dtypes: float64(2), int64(7)
memory usage: 54.1 KB
data.info：
None
8
data_column_X: ['Pregnancies', 'Glucose', 'BloodPressure', 'SkinThickness', 'Insulin', 'BMI', 'DiabetesPedigreeFunction', 'Age']
['Pregnancies', 'Glucose', 'BloodPressure', 'SkinThickness', 'Insulin', 'BMI', 'DiabetesPedigreeFunction', 'Age']1、kNN
kNNC(n_neighbors=9)：Training set accuracy: 0.792
kNNC(n_neighbors=9)：Test set accuracy: 0.7762、逻辑回归
LoR(c_regular=1)：Training set accuracy: 0.785
LoR(c_regular=1)：Test set accuracy: 0.771
3、SVM
SVMC_Init：Training set accuracy: 0.769
SVMC_Init：Test set accuracy: 0.755
SVMC_Best(max_dept=1,learning_rate=0.1)：Training set accuracy: 0.788
SVMC_Best(max_dept=1,learning_rate=0.1)：Test set accuracy: 0.781
DTC(max_dept=3)：Training set accuracy: 0.773
DTC(max_dept=3)：Test set accuracy: 0.740
4、决策树
DTC(max_dept=3)：Training set accuracy: 0.773
DTC(max_dept=3)：Test set accuracy: 0.740
5、随机森林
RFC_Best：Training set accuracy: 0.764
RFC_Best：Test set accuracy: 0.750
6、提升树
GBC(max_dept=1,learning_rate=0.1)：Training set accuracy: 0.804
GBC(max_dept=1,learning_rate=0.1)：Test set accuracy: 0.7817、神经网络
MLPC_Init：Training set accuracy: 0.743
MLPC_Init：Test set accuracy: 0.672