The Pearson’s correlation of linear model (0.5716, 0.5862) is 12 percent higher than Proq2, but still lower than consensus-based method. Actually, almost all of those targets are worse than consensus 4.4.2 Decision Tree Figure 4.4.5 Compare the result of decision tree with Proq2 on dataset one Figure 4.4.6 Compare the result of decision tree with consensus-based method on dataset one When tested on dataset one, the performance of decision tree is comparable with Proq2, for some of the targets Proq2 got a very bad result, decision tree got much better result. But on both quality lost and Pearson’s correlation, decision tree is obviously worse than consensus-based method, and only a few targets got a lower quality lost. 4.4.3 Neural Network Figure 4.4.7 Compare the result of Neural Network with Proq2 on dataset one
The Pearson’s correlation of linear model (0.5716, 0.5862) is 12 percent higher than Proq2, but still lower than consensus-based method. Actually, almost all of those targets are worse than consensus 4.4.2 Decision Tree Figure 4.4.5 Compare the result of decision tree with Proq2 on dataset one Figure 4.4.6 Compare the result of decision tree with consensus-based method on dataset one When tested on dataset one, the performance of decision tree is comparable with Proq2, for some of the targets Proq2 got a very bad result, decision tree got much better result. But on both quality lost and Pearson’s correlation, decision tree is obviously worse than consensus-based method, and only a few targets got a lower quality lost. 4.4.3 Neural Network Figure 4.4.7 Compare the result of Neural Network with Proq2 on dataset one