Tag: Randomly select 80 of the dataset as the training set and the rest as the testing set

Please import the “admit.csv” into Rstudio. In this dataset, we know the GRE score, the GPA, and the…

Please import the "admit.csv" into RStudio. In this dataset, we know the GRE score, the GPA, and the rank of 400 applicants for a graduate program. We also know if each of the candidates is admitted. In the admit column, 1 stands for "admitted", and 0 stands for "rejected". Please answer the following questions and include the codes. 1. Import the dataset and call it "mydata". Then check the structure of the data. 2. Convert the data type of the admit and the rank column from int to factors. 3. Randomly select 80% of the dataset as the training set and the rest as the testing set. 4. Train a decision tree model, using admit as the category, and GRE, GPA, and rank as predictors. Then plot the tree. 5. Please answer the question: if a candidate has a GPA of 3.7 and a rank of 4, does this candidate have a higher chance to be admitted or to be rejected? Please note that when you only have two categories, the darker proportion stands for the proportion for 1 in the end node of the tree plot. 6. Please calculate the accuracy of your decision tree model.

Please import the “admit.csv” into RStudio. In this dataset, we know the GRE score, the GPA, and the rank of 400 applicants for a graduate program. We also know if each of the candidates is admitted. In the admit column, 1 stands for “admitted”, and 0 stands for “rejected”. Please answer the following questions and include the codes. 1. Import the dataset and call it “mydata”. Then check the structure of the data. 2. Convert the data type of the admit and the rank column from int to factors. 3.

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