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Knowledge Discovery with RapidMiner
Published in Richard J. Roiger, Data Mining, 2017
Add the remaining operators in Figure 5.20. Set the maximal decision tree depth at 5. Notice that the Split Data operator shows one output port labeled par, but if you attach this port to the Apply Model operator’s unl (unlabeled) input port, a second par port will appear on Split Data. These ports represent the two partitions of the data. The first partition having two-thirds of the data is for model building, so the top par port should be linked to the Decision Tree operator. The second partition has the remaining data, so the second par port should be linked to the unl input port of the Apply Model operator. The unl connection tells Apply Model that these instances are to be used for model testing.
Decision Trees and Ensemble Methods
Published in Dirk P. Kroese, Zdravko I. Botev, Thomas Taimre, Radislav Vaisman, Data Science and Machine Learning, 2019
Dirk P. Kroese, Zdravko I. Botev, Thomas Taimre, Radislav Vaisman
Example 8.2 (Fixed Tree Depth) To illustrate how the tree depth impacts on the generalization risk, consider Figure 8.4, which shows the typical behavior of the cross-validation loss as a function of the tree depth. Recall that the cross-validation loss is an estimate of the expected generalization risk. Complicated (deep) trees tend to overfit the training data by producing many divisions of the feature space. As we have seen, this overfitting problem is typical of all learning methods; see Chapter 2 and in particular Example 2.1. To conclude, increasing the maximal depth does not necessarily result in better performance. ☞ 26
On Emerging Use Cases and Techniques in Large Networked Data in Biomedical and Social Media Domain
Published in Yulei Wu, Fei Hu, Geyong Min, Albert Y. Zomaya, Big Data and Computational Intelligence in Networking, 2017
Vishrawas Gopalakrishnan, Aidong Zhang
Tree Depth (TD): While GNI calculates the novelty from a topological perspective, it fails to capture the categorical importance behind the node. Thus, the authors use the MeSH tree codes. As mentioned earlier, tree codes denote a categorical hierarchy from generic to specialized concepts. Thus, lower the tree depth value, more generic is the concept. This tree code information of a MeSH term is used to measure the relative importance of a node within a particular category. This measure is a supplement to GNI which is agnostic of category importance. We normalize the tree depth within each category since the maximum tree depth within each category can differ quite drastically.
An ensemble stacked model with bias correction for improved water demand forecasting
Published in Urban Water Journal, 2020
Maria Xenochristou, Zoran Kapelan
There are three main parameters that need tuning in RFs, the mtry, ntrees, and tree depth (Scornet 2017). The mtry is the number of variables randomly selected at each node and considered for splitting. Reducing the mtry increases the randomness of the tree-building process and therefore creates trees that are less similar to each other. The ntrees parameter is the number of trees used to build the forest. Model accuracy typically plateaus after a number of trees that are required to build a credible model. The tree depth is the point at which the tree stops growing, sometimes also denoted by the size of the final tree node (nodesize). The higher the tree depth, the closer the model fits on the training data, thus increasing the risk of overfitting. The RF model is tuned for the optimum values of all of the above three parameters.
A gradient boosting approach to understanding airport runway and taxiway pavement deterioration
Published in International Journal of Pavement Engineering, 2021
Limon Barua, Bo Zou, Mohamadhossein Noruzoliaee, Sybil Derrible
To answer the question, let us first have some intuitive understanding on why these hyperparameters are important. For , having too few trees (small ) will obviously not produce a good model fit. When more trees are added (larger ), the GBM model will become more complex and have improved fit with the training data. However, fitting the training data too closely can be counterproductive as it often leads to poor generalisation ability – i.e. overfitting. Therefore, an optimal needs to be determined. In doing so, a tradeoff should be recognised between and since both parameters affect the model prediction error (Friedman 2001): to achieve the same model fit, a small value of (which means “shrinking” the model fit) requires a large . For the maximum depth of a tree, the greater the depth, the larger the possible number of terminal nodes in a tree, which allows for capturing higher-order interaction effects among the input variables. Nevertheless, capturing more than the actual interaction effects can lead to overfitting (Hastie et al. 2009). Once the maximum depth of a tree is fixed, the actual tree depth may be determined using cost-complexity pruning. Finally, requiring the minimum number of observations under a node is reasonable: we will not want too few observations after the node is split.