一周算法实践__1.模型构建

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数据集下载：https://pan.baidu.com/s/1dtHJiV6zMbf_fWPi-dZ95g

1.导入模块

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import numpy as np
import pandas as pd

from sklearn.model_selection import train_test_split,cross_val_score
from sklearn.linear_model import LogisticRegression
from sklearn.svm import SVC
from sklearn.tree import DecisionTreeClassifier
from sklearn.metrics import f1_score,accuracy_score,recall_score

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2.划分X和y并简单分析数据

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X_train,X_test,y_train,y_test=train_test_split(X,y,test_size=0.3,random_state=2018)
print(len(X_train))
print(len(X_test))
print(len(y_test[y_test==0])/len(y_test))

3327
1427
0.7484232655921513

训练集数量：测试集数量=3:1

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3.构建模型进行预测

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# LogisticRegression模型
clf_Lr=LogisticRegression(random_state=0,solver='lbfgs').fit(X_train,y_train)
y_test_pred=clf_Lr.predict(X_test)
Lr_acc=accuracy_score(y_test,y_test_pred)
f1=f1_score(y_test,y_test_pred,average='micro')
print(f1)
print(Lr_acc)
print(np.unique(y_test_pred))

0.7484232655921513
0.7484232655921513
[0]

#SVM模型
clf_SVM=SVC(gamma='auto').fit(X_train,y_train)
y_test_pred=clf_SVM.predict(X_test)
SVM_acc=accuracy_score(y_test,y_test_pred)
f1=f1_score(y_test,y_test_pred,average='micro')
print(f1)
print(SVM_acc)
print(np.unique(y_test_pred))

0.7484232655921513
0.7484232655921513
[0]

#决策树模型
clf_Tree=DecisionTreeClassifier(random_state=0).fit(X_train,y_train)
y_test_pred=clf_Tree.predict(X_test)
Tree_acc=accuracy_score(y_test,y_test_pred)
f1=f1_score(y_test,y_test_pred,average='micro')
print(f1)
print(Tree_acc)
print(np.unique(y_test_pred))

0.6629292221443588
0.6629292221443588
[0 1]

通过对比可以看出，LR模型和SVM模型的准确率相同，决策树模型的准确率略低。但是LR模型和SVM模型都将测试集中样本预测为 0 ，分析可得len(y_test[y_test==0])/len(y_test)=0.7484232655921513。因此选用决策树模型。