They also reported that RF produced the most consistent results with very good predictive ability and outperformed other methods in terms of correct classification. González-Recio and Forni ( 2011) found that RF performed better than Bayesian regression when detecting resistant and susceptible animals based on genetic markers. ( 2016) found that, for binary traits, RF outperformed the GBLUP method only in a scenario combining the highest heritability, the largest dense marker panel (50K SNP chip), and the largest number of QTL. ( 2009) found that RF performs better than other methods for binary traits when the sample size is large and the percentage of missing data is low (García-Magariños et al. RF is one of the models adopted for genomic prediction with many successful applications (Sarkar et al. For these reasons, RF is one of the most popular and powerful machine learning algorithms that has been successfully applied in fields such as banking, medicine, electronic commerce, stock market, and finance, among others.ĭue to the fact that there is no universal model that works in all circumstances, many statistical machine learning models have been adopted for genomic prediction. Also, RF allows measuring the relative importance of each predictor (independent variable) for the prediction. RF is a supervised machine learning algorithm that is very flexible, easy to use, and that without a lot of effort produces very competitive predictions of continuous, binary, categorical, and count outcomes. Random forest (RF) has proven to be an effective tool for such settings, already having produced numerous successful applications (Chen and Ishwaran 2012). The complexity and high dimensionality of genomic data require using flexible and powerful statistical machine learning tools for effective statistical analysis. Final comments about the pros and cons of random forest are provided. In this case, some examples are provided for illustrating its implementation even with mixed outcomes (continuous, binary, and categorical). The random forest algorithm for multivariate outcomes is provided and its most popular splitting rules are also explained. In addition, many examples are provided for training random forest models with different types of response variables with plant breeding data. We give (1) the random forest algorithm, (2) the main hyperparameters that need to be tuned, and (3) different splitting rules that are key for implementing random forest models for continuous, binary, categorical, and count response variables. Then we describe the process of building decision trees, which are a key component for building random forest models. The motivations for using random forest in genomic-enabled prediction are explained. Copyright © 2023 The Author(s) Open Access Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License ( ), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.We give a detailed description of random forest and exemplify its use with data from plant breeding and genomic selection. This research can serve as a reference for future researchers in the housing rental market, while helping landlords and tenants make optimal choices. The prediction effect of rental price in Wuhan is significantly better, among which the characteristics of urban area and housing itself have a great impact on rental price. It may be that for a highly modernized first-tier city, the variables selected in this paper are not enough to fully explain the rental price. Finally, it is found that the random forest regression model has no significant effect on the rental forecast in Shanghai. Based on the random forest model, this paper selects two cities, Shanghai and Wuhan, to study the price trend of the housing rental market and its influencing factors. However, the high housing prices in the first-tier and new first-tier cities have forced many young people to turn their attention to the rental market, setting off an upsurge of housing rental. With the rapid development of China's real estate market, the real estate industry has become a significant part of Chinese national economy.
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