Logistic regression techniques based on different sample sizes in landslide susceptibility assessment: which performs better?
Keywords:
landslide susceptibility, penalty term, machine learning, ridge logistic regression, lasso logistic regression, receiver operating characteristicAbstract
The main objective of this paper is to compare the landslide spatial prediction performance of logistic regression (LR) with different regularization methods, namely, Lasso LR and Ridge LR. Three types of training datasets with different sample sizes of 40,000, 4,000 and 400 are used to train and validate the models. ROC curves are used to evaluate the models’ performance. The results show that Lasso and Ridge LR models have comparative performance compared to the ordinary LR models based on the AUC values, which indicates that there are no redundant input features to remove from the models for the available data in this work to some degree. The penalty terms play a negligible role in the LR models trained with the three types of datasets. Lasso LR has a better performance than ridge LR, which may be due to that the L1 penalized parameter which can be exactly equal to zero. According to the AUC values, the group of models trained and validated using the dataset of 20,000 samples outperform the other two groups.
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