Selected article for: "dataset split and randomly dataset split"
Author: Jit Sarkar; Partha Chakrabarti
Title: A Machine Learning Model Reveals Older Age and Delayed Hospitalization as Predictors of Mortality in Patients with COVID-19
  • Document date: 2020_3_30
    • ID: 0agldesf_7
    Snippet: Random Forest classification algorithm (4) was implemented over a dataset with 37 deaths and 78 recoveries using the randomForest package in R. The dataset was randomly split into training and test dataset containing 70% and 30% of the total samples respectively. To evaluate the model performance, the Area under the ROC curve was calculated on the test dataset. A variable importance plot was generated using the importance of the predictors over t.....
    Document: Random Forest classification algorithm (4) was implemented over a dataset with 37 deaths and 78 recoveries using the randomForest package in R. The dataset was randomly split into training and test dataset containing 70% and 30% of the total samples respectively. To evaluate the model performance, the Area under the ROC curve was calculated on the test dataset. A variable importance plot was generated using the importance of the predictors over the outcome. The importance of the variables has been reported according to both the mean decrease of Gini and the mean decrease of Accuracy. The partial dependency plots were finally generated using the pdp package in R to determine the marginal effect of the Age and Time to Hospitalization over the fate of COVID-19 infection.

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