![]() Therefore, the optimized model can generate a high-quality landslide susceptibility map.Ī landslide is defined as the movement of a mass of rock, earth, or debris down a slope ( Cruden, 1991). Thus, hyperparameter optimization is of great significance in the improvement of the prediction accuracy of the model. However, the random forest model has a higher predictive ability than the extreme gradient boosting decision tree model. The results show that the AUC validation data of the Bayesian optimized random forest and extreme gradient boosting decision tree model are 0.88 and 0.86, respectively, which showed an improvement of 4 and 3%, indicating that the prediction performance of the two models has been improved. Both models were evaluated and compared using the receiver operating characteristic curve and confusion matrix. The hyperparameters of the random forest and extreme gradient boosting decision tree models were optimized using a Bayesian algorithm, and then the optimal hyperparameters are selected for landslide susceptibility mapping. The landslides were randomly divided into training data (70%) and validation data (30%). In addition, 14 landslide influencing factors are selected, and 734 landslides are obtained according to field investigation and reports from literals. Taking Wuqi County in the hinterland of the Loess Plateau as the research area, using Bayesian hyperparameters to optimize random forest and extreme gradient boosting decision trees model for landslide susceptibility mapping, and the two optimized models are compared. Landslides are widely distributed worldwide and often result in tremendous casualties and economic losses, especially in the Loess Plateau of China. College of Geological Engineering and Geomatics/Key Laboratory of Western China Mineral Resources and Geological Engineering, Chang’an University, Xi’an, China. ![]() Note that “gain” would be the most similar to what I said before.Shibao Wang Jianqi Zhuang* Jia Zheng Hongyu Fan Jiaxu Kong Jiewei Zhan Where coverage is defined as the number of samples affected by the split. “cover” is the average coverage of splits which use the feature “gain” is the average gain of splits which use the feature. “weight” is the number of times a feature appears in a tree. How the importance is calculated: either “weight”, “gain”, or “cover”. It is sometimes called “gini importance” or “mean decrease impurity” and is defined as the total decrease in node impurity (weighted by the probability of reaching that node (which is approximated by the proportion of samples reaching that node)) averaged over all trees of the ensemble.įor XGBoost, the ot_importance method gives the following options to plot the variable importances: There they quote this post in Stack Overflow explaining how the above mechanism is implemented in scikitlearn: For Random Forest, I recommend you read this cool post from Jeremy and Terence explaining the perils of this technique and why they prefer another mechanism, permutation importance. More practical one can come from the docs of the respective libraries. That is the best concise theoretical explanation that I can do. Then, average across all the trees used in each ensemble. Thus, both Random Forest and XGBoost generalize this method: for each tree, do the method above. The squared relative importance of variable is the sum of such squared improvements over all internal nodes for which it was chosen as the splitting variable.> The particular variable chosen is the one that gives maximal estimated improvement () in … risk over that for a constant fit over the entire region. The feature importance in both cases is the same: given a tree go over all the nodes of the tree and do the following: ( From the Elements of Statistical Learning p.368 (freely available here)):Īt each such node t, one of the input variables Xv(t) is used to partition the region associated with that node into two subregions within each a separate constant is fit to the response values.
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