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

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


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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: 0.776

2、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: 0.781

7、神经网络

MLPC_Init:Training set accuracy: 0.743
MLPC_Init:Test set accuracy: 0.672
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