format(test_accuracy)) # Compute confusion matrix conf_matrix = confusion_matrix(y_test, y_pred) print("Confusion Matrix:") print(conf_matrix) # Visualize confusion matrix sns.heatmap(conf_matrix, annot=True, c
You can use the plot_confusion_matrix method to visualize the confusion matrix.1 2 3 4 5 6 7 8 9 10 11 import matplotlib.pyplot as plt from sklearn.metrics import plot_confusion_matrix color = 'white' matrix = plot_confusion_matrix(knn, X_test, y_test, cmap=plt.cm.Blues) matrix....
plt.title('Confusion Matrix') plt.xlabel('Predicted label') plt.ylabel('True label') plt.show() #进行ROC曲线绘制计算准备 # у得分为模型预测正例的概率 y_score =nnmodel.predict_proba(X_test)[:,1] #计算不同阈值下,fpr和tpr的组合值,其中fpr表示1-Specificity,tpr表示sensitivity fpr,tpr,thres...
Today is last for you to confuse about confusion matrix. Before that, how many times your read about confusion matrix, and after a while forgot about theture positive, false negative... etc, Even you implemented confusion matrix with sklearn or tensorflow, Still we get confusion about the ea...
sns.heatmap(metrics.confusion_matrix(y_test, y_pred), annot=True, fmt='d') plt.title('Confusion Matrix') plt.xlabel('Predicted label') plt.ylabel('True label') plt.show() #进行ROC曲线绘制计算准备 # у得分为模型预测正例的概率 y_score =nnmodel.predict_proba(X_test)[:,1] #计算不...
# confusion_matrix.add(outputs.detach(), labels.detach()) # statistics # 计算`running_loss`和`running_corrects` #loss.item()得到的是此时损失loss的值 #inputs.size(0)得到的是一批图片的数量,这里为4 #两者相乘得到的是4张图片的总损失
confusion_matrix: 计算混淆矩阵。 classification_report: 输出精确度、召回率和F1 score。 7. 结果可视化 结果的可视化有助于更直观地理解模型的表现。 importseabornassnsimportmatplotlib.pyplotasplt# 可视化混淆矩阵plt.figure(figsize=(8,6))sns.heatmap(cm,annot=True,fmt='d',cmap='Blues',xticklabels=iri...
print(confusion_matrix(y_test, y_pred)) 五、优化模型参数 为了提高模型的性能,我们可以通过网格搜索(Grid Search)和交叉验证(Cross Validation)来优化模型参数。scikit-learn库中的GridSearchCV可以帮助我们实现这一点: from sklearn.model_selection import GridSearchCV ...
from sklearn.svm import SVC clf = SVC(C=1, kernel='poly', degree=2) clf.fit(train_data,train_labels) print(clf.score(test_data,test_labels)) # 0.9806 test_predictions = clf.predict(test_data) cm = metrics.confusion_matrix(test_labels,test_predictions) df_cm = pd.DataFrame(cm, rang...
The integration allows you to validate outputs, iterate quickly, and visualize your prompt logic as you go. This tight coupling makes BAML a natural choice for developers looking to streamline their LLM workflows while maintaining precision and type safety. Check out the Playground to get a ...