爱站程序员基地ML之Classification：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)实现二分类预测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
数据集理解
1、kNN
2、逻辑回归
3、SVM
4、决策树
5、随机森林
6、提升树
7、神经网络
相关文章
ML之Classification：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)实现二分类预测案例来理解和认知机器学习分类预测的模板流程
ML之Classification：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)实现二分类预测案例来理解和认知机器学习分类预测全部
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
[code]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 Outcome0 6 148 72 ... 0.627 50 11 1 85 66 ... 0.351 31 02 8 183 64 ... 0.672 32 13 1 89 66 ... 0.167 21 04 0 137 40 ... 2.288 33 1[5 rows x 9 columns]<class \'pandas.core.frame.DataFrame\'>RangeIndex: 768 entries, 0 to 767Data columns (total 9 columns):# Column Non-Null Count Dtype--- ------ -------------- -----0 Pregnancies 768 non-null int641 Glucose 768 non-null int642 BloodPressure 768 non-null int643 SkinThickness 768 non-null int644 Insulin 768 non-null int645 BMI 768 non-null float646 DiabetesPedigreeFunction 768 non-null float647 Age 768 non-null int648 Outcome 768 non-null int64dtypes: float64(2), int64(7)memory usage: 54.1 KBdata.info：None8data_column_X: [\'Pregnancies\', \'Glucose\', \'BloodPressure\', \'SkinThickness\', \'Insulin\', \'BMI\', \'DiabetesPedigreeFunction\', \'Age\'][\'Pregnancies\', \'Glucose\', \'BloodPressure\', \'SkinThickness\', \'Insulin\', \'BMI\', \'DiabetesPedigreeFunction\', \'Age\']
1、kNN
[code]kNNC(n_neighbors=9)：Training set accuracy: 0.792kNNC(n_neighbors=9)：Test set accuracy: 0.776
2、逻辑回归
[code]LoR(c_regular=1)：Training set accuracy: 0.785LoR(c_regular=1)：Test set accuracy: 0.771
3、SVM
[code]SVMC_Init：Training set accuracy: 0.769SVMC_Init：Test set accuracy: 0.755SVMC_Best(max_dept=1,learning_rate=0.1)：Training set accuracy: 0.788SVMC_Best(max_dept=1,learning_rate=0.1)：Test set accuracy: 0.781DTC(max_dept=3)：Training set accuracy: 0.773DTC(max_dept=3)：Test set accuracy: 0.740
4、决策树
[code]DTC(max_dept=3)：Training set accuracy: 0.773DTC(max_dept=3)：Test set accuracy: 0.740
5、随机森林
[code]RFC_Best：Training set accuracy: 0.764RFC_Best：Test set accuracy: 0.750
6、提升树
[code]GBC(max_dept=1,learning_rate=0.1)：Training set accuracy: 0.804GBC(max_dept=1,learning_rate=0.1)：Test set accuracy: 0.781
7、神经网络
[code]MLPC_Init：Training set accuracy: 0.743MLPC_Init：Test set accuracy: 0.672
