@article{Saboori_Doostparast_2023, title={Random forests in the zero to k inflated Power series populations}, volume={11}, url={http://iapress.org/index.php/soic/article/view/1773}, DOI={10.19139/soic-2310-5070-1773}, abstractNote={<p><span class="fontstyle0">Tree-based algorithms are a class of useful, versatile, and popular tools in data mining and machine learning.<br>Indeed, tree aggregation methods, such as random forests, are among the most powerful approaches to boost<br>the performance of predictions. In this article, we apply tree-based methods to model and predict discrete<br>data, using a highly flexible model. Inflation may occur in discrete data at some points. Inflation can be<br>at points as zero, one or the other. We may even have inflation at two points or more. We use models for<br>inflated data sets based on a common discrete family (the Power series models). The Power series models<br>are one of the most famous families used in such models. This family includes common discrete models such<br>as the Poisson, Negative Binomial, Multinomial, and Logarithmic series models.<br>The main idea of this article is to use zero to </span><span class="fontstyle2">k </span><span class="fontstyle0">(</span><span class="fontstyle2">k </span><span class="fontstyle3">= </span><span class="fontstyle4">0</span><span class="fontstyle5">, </span><span class="fontstyle4">1</span><span class="fontstyle5">, . . .</span><span class="fontstyle0">) inflated regression models based on the family<br>of power series to fit decision regression trees and random forests. An important point of these models is<br>that they can be used not only for inflated discrete data but also for non-inflated discrete data. Indeed this<br>model can be used for a wide range of discrete data sets.</span></p>}, number={4}, journal={Statistics, Optimization & Information Computing}, author={Saboori, Hadi and Doostparast, Mahdi}, year={2023}, month={Aug.}, pages={865-875} }