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:
0.776
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
转载：https://blog.csdn.net/qq_41185868/article/details/108342397
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