詹小可项目简单代码

import scipy.io as scio

data = scio.loadmat('1.mat')

test_data = data['yeast_f1_test_feature']

test_label = data['yeast_f1_test_label']

train_data = data['yeast_f1_train_feature']

train_label = data['yeast_f1_train_label']

'''
算法包调用
'''
from sklearn import tree
from sklearn import svm
'''
'''
import numpy as np

import pandas as pd

clf = tree.DecisionTreeClassifier(max_depth=55)   #决策树
#clf=svm.SVC(decision_function_shape='ovo')


clf.fit(train_data,train_label)

predict = clf.predict(test_data)

result = pd.DataFrame()


result['predict'] = predict

result['test_label'] = test_label.reshape(test_label.shape[0],1)


from sklearn.metrics import accuracy_score    #准确率

from sklearn.metrics import precision_score   #查准率      精确率

from sklearn.metrics import recall_score      #灵敏度      召回率

from sklearn.metrics import matthews_corrcoef #马修斯相关系数

print('总体预测精度: '+str(accuracy_score(result['test_label'],result['predict'])))
print('精度: '+str(precision_score(result['test_label'],result['predict'])))
print('灵敏度: '+str(recall_score(result['test_label'],result['predict'])))
print('马修系数: '+str(matthews_corrcoef(result['test_label'],result['predict'])))