![]() The algorithm works as follows: Sample m. ![]() Hence, the proposed model demonstrates superior predictive power over other benchmark models.Īrtificial intelligence COVID-19 broad learning system (BLS) coronavirus disease 2019 (COVID-19) testing capacity random forest (RF) time-series forecasting. A Random Forest is essentially nothing else but bagged decision trees, with a slightly modified splitting criteria. In addition, we compared the forecasting results with linear regression (LR) model, -nearest neighbors (KNN), decision tree (DT), adaptive boosting (Ada), RF, gradient boosting DT (GBDT), support vector regression (SVR), extra trees (ETs) regressor, CatBoost (CAT), LightGBM (LGB), XGBoost (XGB), and BLS.The RF-Bagging BLS model showed better forecasting performance in terms of relative mean-square error (RMSE), coefficient of determination (), adjusted coefficient of determination (), median absolute error (MAD), and mean absolute percentage error (MAPE) than other models. Then, we combine the bagging strategy and BLS to develop a random-forest-bagging BLS (RF-Bagging-BLS) approach to forecast the trend of the COVID-19 pandemic. Random Forests (RF) Train an RF regressor Evaluate the RF regressor Visualizing features importances import pandas as pd import numpy as np import matplotlib. Here, we leveraged random forest (RF) to screen out the key features. Random Forest is a Bagging technique, so. Moreover, by working with a random sample of predictors at each possible split, the fitted values across trees are more independent. We proposed a machine learning model for COVID-19 prediction based on the broad learning system (BLS). Random Forest is a Supervised learning algorithm that is based on the ensemble learning method and many Decision Trees. It is apparent that random forests are a form of bagging, and the averaging over trees can substantially reduce instability that might otherwise result. In this work, a large COVID-19 data set consisting of COVID-19 pandemic, COVID-19 testing capacity, economic level, demographic information, and geographic location data in 184 countries and 1241 areas from December 18, 2019, to September 30, 2020, were developed from public reports released by national health authorities and bureau of statistics. Recently, the analysis and forecast of the COVID-19 pandemic have attracted worldwide attention. Random forests are a combination of tree predictors such that each tree depends on the values of a random vector sampled independently and with the same distribution for all trees in the forest. tl dr: Bagging and random forests are bagging algorithms that aim to scale back the complexity of models that overfit the training data. The rapid geographic spread of COVID-19, to which various factors may have contributed, has caused a global health crisis.
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