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Here, we define the maximal depth of trees as five in RPMF.
Specially, the maximal depth of trees over Food dataset is two in SoCo.
Open image in new window Fig. 6 Impact of depth of trees over Food dataset.
Both the number and depth of trees have important impact on the decision tree-based prediction methods.
In SoCo, the maximal depth of trees equals the number of contextual variables excluding user and item.
Then the maximal depth of trees over Food dataset is four in RPFM. Figure 6 shows that the deeper of trees, the better prediction quality, and RPFM outperforms RPMF and SoCo in terms of MAE and RMSE.
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Consequently, it is a capability to embody classifying by setting a higher depth of tree.
Shown in Fig. 3, with the depth of tree higher, the recognition rate of DT becomes better.
This time is also proportional to the number of nodes in the network, that is, the depth of tree.
And then the experiment starts from the comparison between the AdaBoost algorithm and decision tree algorithm with uncertain depth of tree.
All of the results based on three depth of tree are done with 100 rounds except xgboost that only iterates 20 times.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com