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(criterion='entropy', max_depth=28,max_leaf_nodes=1000) ...
(DTCMM) alongside the utilization of the AHP and DEMATEL method for a comprehensive and accurate analysis of digital transformation maturity; Section 4 uses the AHP-DEMATEL method to analyze digital transformation maturity in construction enterprises, demonstrating progress and the need for enhancement ...
DTC employs hysteresis control and determines the optimal switching state of the inverter, such that the torque and control winding flux λcλc stay within a hysteresis band around their reference. Each switching state can be mapped with a certain impact on the control winding flux, both on ...
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...