#绘制混淆矩阵；可视化混淆矩阵
from sklearn import metrics
print(metrics.classification_report(train_y,train_predicted))
"""
可视化混淆矩阵
def cm_plot(y,yp):
from sklearn.metrics import confusion_matrix
from matplotlib.pyplot as plt
cm = confusion_matrix(y,yp)
plt.matshow(cm,cmap=plt.com.Blues)
plt.colorbar()
for x in range(len(cm)):
for y in range(len(cm)):
plt.annotate(cm[x,y],xy=(y,x),horizontalalignment='centry',
verticalalignment='centry')
plt.ylabel('True label')
plt.xlabel('Predicted label')
return plt
cm_plot(train_y,train_predicted).show()
使用测试数据进行测试
"""
test_data = pd.read_excel(r'C:UsersLenovoDesktop鸾尾花.xlsx',encoding='utf8')
test_x=train_data[['sepallengthcm(cm)','sepalwidth(cm)','petallength(cm)','petalwidth(cm)']]
test_y=train_data[['leixing']]
test_predicted=model.predict(test_x)
test_predicted_pr=model.predict_proba(test_x)
from sklearn import metrics
print(metrics.classification_report(test_y,test_predicted))
#可视化混淆矩阵
cm_plot(test_y,test_predicted).show()
