dtc.fit(X_train,y_train) pred_dtc=dtc.predict(X_test) print("d=",d) print(accuracy_score(y_test,pred_dtc)) 当max_depth设置为28,max_leaf_nodes设置为1000并且使用熵作为准则时,决策树分类器表现最好。 dtc_best=DecisionTreeClassifier(criterio
The cross-entropy from (1) remains the loss function for training. Figure 3c shows the decision boundary for the cosine softmax classifier. After training, all the sample vectors were normalized to the unit length; they not only moved away from the inter-category boundaries, but also converged...
The cross-entropy from (1) remains the loss function for training. Figure 3c shows the decision boundary for the cosine softmax classifier. After training, all the sample vectors were normalized to the unit length; they not only moved away from the inter-category boundaries, but also converged...