Assuming doing a 10-fold cross validation for linear regression (Y = AX + B). It is possible that 10 different As and Bs would be generated, one for each model learned.单项选择题
A
True
B
False

Question

Assuming doing a 10-fold cross validation for linear regression (Y = AX + B).  It is possible that 10 different As and Bs would be generated, one for each model learned.

BlackTom AI · Accepted Answer

Here is how to think about the question and each option in turn.
Option 1: True. In 10-fold cross-validation for linear regression, the training data for each fold typically differs from the others because the data is partitioned into 10 distinct training sets (each leaving out a different 1/10th subset for validation). Since the least squares solution (the coefficients A and intercept B) depends on the particular training data, it is entirely possible for each fold to yield a different pair of coefficients. Even though the underlying model form is the same, the estimated parameters can change because the input data used to fit the model changes across folds.
Option 2: False. This would claim that it is impossible to obtain different A and B across folds. However, unless every training set is identical (which would defeat the purpose of cross-validation), the training data across folds are not the same, so the resulting parameter estimates can differ. Therefore, this statement contradicts the fundamental behavior of cross-validated model fitting and is incorrect.
Additional nuance: while it is possible that some folds produce identical coefficients (for example, if the added variation in the training set does not affect the least-squares solution due to collinearity or identical design matrices in certain folds), the general expectation in 10-fold CV is that you will obtain a variety of parameter estimates across folds because the training sub-datasets are different. This supports the notion that multiple different (A, B) pairs can be learned across folds.
In summary, given the standard cross-validation procedure with varying training data across folds, the statement that 10 different A and B could be generated is consistent with how the estimates are derived and is therefore true.

Assuming doing a 10-fold cross validation for linear regression (Y = AX + B). It is possible that 10 different As and Bs would be generated, one for each model learned.单项选择题

类似问题

In a 75%/25% Cross-Validation

Please select the incorrect statements about k-fold cross-validation and Leave-One-Out Cross-Validation (LOOCV):

Cross-validation is a special case of the validation set approach.

Suppose we are going to perform K-fold cross-validation. If we make 5 folds from the data set that has 100 observations. Which of the following are true statements?

Which of the following approaches is best when deciding what model to use, e.g. when deciding what polynomial degree to use in regression?

A 62.9 kg lady is standing on a weighing scale in an elevator as shown in the diagram below. Assume gravitational acceleration = 15 ms-2 Given that the elevator is accelerating upwards at 3.7 ms-2 , determine the magnitude of the normal force on the lady.

An object is fired from ground level at an angle of 31.9 degrees to the horizontal. Taking acceleration due to gravity = 7.65 ms-2 If the initial speed is 5.6 ms-1, how far from the starting point will it hit the ground? Assume air resistance is negligible.

Which of the following equations of motion would be used to calculate the displacement given the initial and final velocities as well as the acceleration?

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