Before running a regression, a researcher uses the command 'index

Question

Before running a regression, a researcher uses the command 'index <- sample(seq(1:2),1:nrow(df), prob=c(.7,.3), replace=TRUE)'. What is the primary purpose of this step?

BlackTom AI · Accepted Answer

The command randomly assigns each row of df to one of two groups with probabilities 0.7 and 0.3, so about 70% of rows are labeled “1” and 30% labeled “2” for later subsetting into train/test sets.

1) Relevant knowledge points and concepts
- R’s sample() function: sample(x, size, prob, replace) draws items from x with given probabilities. seq(1:2) (or 1:2) is the vector c(1,2).
- Train/test splitting: partitioning data into a training set to fit a model and a testing/validation set to evaluate predictive performance and avoid overfitting.
- replace=TRUE with prob makes each draw independent and identically distributed according to the specified probabilities.

2) Step-by-step reasoning
- seq(1:2) produces c(1,2).
- size = 1:nrow(df) actually yields an integer vector of length nrow(df); in sample() the intended call is sample(1:2, nrow(df), prob=c(.7,.3), replace=TRUE). The effect is one random draw per row.
- Each row gets an index value 1 (with prob 0.7) or 2 (with prob 0.3).
- Typical usage: train <- df[index==1, ] and test <- df[index==2, ].

3) Intermediate calculation example
- If nrow(df) = 100, expected rows labeled 1 ≈ 0.7*100 = 70, labeled 2 ≈ 0.3*100 = 30.
- These are expectations; actual counts vary by randomness.

4) Why the correct answer is correct
- The code’s aim is to create a probabilistic split of observations into two groups (commonly training and testing). Using the resulting index to subset df yields a 70/30 train/test partition for model building and evaluation.

5) Why other options are incorrect
- “Identify and remove 70% of missing data”: the command does not inspect or remove missing values.
- “Select a random subset of variables”: it assigns rows to groups, not variables/columns.
- “Increase R-squared by reducing sample size”: splitting data does not purposefully increase R-squared and the code’s intent is evaluation, not manipulating fit statistics.

6) Conclusion
This command is used to create a 70% training and 30% testing/validation dataset, matching the provided correct answer.

Before running a regression, a researcher uses the command 'index <- sample(seq(1:2),1:nrow(df), prob=c(.7,.3), replace=TRUE)'. What is the primary purpose of this step?单项选择题

类似问题

What function randomly splits a data set into training and testing subsets?

What is the purpose of performing a train/test split?

What is the purpose of performing a train/test split?

Consider the following Python code: X_train, X_test, y_train, y_test = train_test_split( X, y, test_size=0.2, random_state=42 ) If the original dataset contains 1,000 observations, how many will be in the training set?

In the prac, what does test_size=0.4 in train_test_split() mean?

Random occurrences in data mining results can be detected by dividing the data and building a model with one portion and using another portion to verify the results.

What is the primary purpose of splitting data into training and testing sets in predictive analytics?

Evaluating algorithms (1 mark)Complete this sentence. When evaluating algorithms, training and test sets usually:[Fill in the blank]

更多留学生实用工具

智能学习助手

风格化写作助手

论文查重助手

文献引用助手

课堂转译助手

课堂笔记助手

Quiz搜索助手

学校历年真题

智能刷题助手

智能匹配练习

希望你的学习变得更简单