The logistic regression model links the conditional probability P(Y=1|X1,...,Xp) to X1,…,Xp through. The previous section showed that benchmarking results in subgroups may be considerably different from that of the entire datasets collection. RF performed better than LR according to the considered accuracy measured in approximately 69% of the datasets. Based on these datasets’ characteristics, we define subgroups and repeat the benchmark study within these subgroups, following the principle of subgroup analyses in clinical research. Bischl B, Lang M, Kotthoff L, Schiffner J, Richter J, Jones Z, Casalicchio G. Mlr: Machine Learning in R. 2016. Our analyses reveal a noticeable influence of the number of features p and the ratio \(\frac {p}{n}\). Cite this article. They are running models within each node. Evol Comput. Other Classification Algorithms 8. Large nodesize values seem to perform slightly better (this is in line with the output of tuneRanger, which selects 17 as the optimal nodesize value), while there is no noticeable trend for mtry and sampsize. The most correct answer as mentioned in the first part of this 2 part article , still remains it depends. The superiority of RF may be more pronounced if used together with an appropriate tuning strategy, as suggested by our additional analyses with TRF. This points out the importance of the definition of clear inclusion criteria for datasets in a benchmark experiment and of the consideration of the meta-features’ distributions. Setting this number larger yields smaller trees. The commonly used threshold c=0.5, which is also used in our study, yields a so-called Bayes classifier. And another seemingly obvious explanatory variable is quantity: The higher the quantity of water the higher the probability that we have ourselves a functioning waterpoint. Couronné R, Probst P. 2017. https://doi.org/10.5281/zenodo.439090https://doi.org/10.5281/zenodo.439090. By presenting the results on the average superiority with default values over LR, we by no means want to definitively establish these default values. In a k-fold cross-validation (CV), the original dataset is randomly partitioned into k subsets of approximately equal sizes. Although these results should be considered with caution, since they are possibly highly dependent on the particular distribution of the meta-features over the 243 datasets and confounding may be an issue, we conclude from “Explaining differences: datasets’ meta-features” section that meta-features substantially affect Δacc. These important aspects are not taken into account in our study, which deliberately focuses on prediction accuracy. Top: boxplot of the performance of LR (dark) and RF (white) for each performance measure. 2. Top: Boxplot of the performance (acc) of RF (dark) and LR (white) for N=50 sub-datasets extracted from the OpenML dataset with ID=310 by randomly picking n′≤n observations and p′

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