Consider a labeled data set containing 100 data instances, which is randomly partitioned into two sets A and B, each containing 50 instances. We use A as the training set to learn two decision trees, D10 with 10 leaf nodes and D100 with 100 leaf nodes. The accuracies of the two decision trees on data sets A and B are shown in the following table. Accuracies of two different decision trees Dataset D10 D100 Accuracy A (training) 0.86 0.97 ІВ B (validation) 0.84 0.77 (a) Based on the accuracies shown in the table, which classification model would you expect to have better performance on unseen instances? Write 10 for D₁0 and 100 for D100-

Consider a labeled data set containing 100 data instances, which is randomly partitioned into two sets A and B, each containing 50 instances. We use A as the training set to learn two decision trees, D10 with 10 leaf nodes and D100 with 100 leaf nodes. The accuracies of the two decision trees on data sets A and B are shown in the following table. Accuracies of two different decision trees Dataset D10 D100 Accuracy A (training) 0.86 0.97 ІВ B (validation) 0.84 0.77 (a) Based on the accuracies shown in the table, which classification model would you expect to have better performance on unseen instances? Write 10 for D₁0 and 100 for D100-