Which of the Following Statements is True?

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In this article, you can grab more information about which of the following statements is true. Sentences can be true or false, and things can get complicated when answering them. You can find here Which of the following statements is true? and you have to answer it correctly.

Which of The Following Statements Is True? – FAQ

1. What is a Statement?

A statement is declarative sentence that is true or false or proposition.

2. How do you determine whether the statement is true or false?

Something can statement, and we don’t know whether it’s true or false. If you don’t know whether statement is true or false, think out of the box to get the answer.

3. Is a statement a sentence?

A sentence can be statement, command, or question. An argument is a kind of sentence.

Here are Some points on which of the following statements is true.

(A) The shares of a public limited company are not freely transferable.

(B) Paid-up capital is part of subscribed capital, which has called up.

(C) The company cannot raise more capital than the amount specified in the Memorandum of Association.

(D) Capital Reserve is The part of uncalled capital that is called only in the event of winding up of the company.

Which of the following statement is TRUE?

1. For generalization, we should grow the tree entirely to get good training accuracy.
2. To get good training accuracy, we can ultimately grow trees and then prune them back.
3. We measure the performance of a tree by calculating the fraction of training instances that are correctly classified.
4. Test set should never used in training the tree.

• Generalization is not training accuracy.
• That is for generalization.
• It should be on future unseen data like test set
• Right, so that we can get an unbiased estimation of the accuracy.

Q2-1: Which of the following statements is TRUE?

1. If there is no noise, there is no overfitting.
2. Overfitting may improve the generalization ability of model.
3. Generalization error is monotone concerning the capacity/complexity of the model.
4. More training data may help prevent overfitting.

• We still have false correlation that lead to overfitting.
• Overfitting undermine the generalization ability.
• Generalization error would first decrease and then increase as the model capacity increases.
• Increasing training data size would help better approximate the proper distribution.

Q2-2: Which of the following may not lead to overfitting?

1. Limited training data.
2. Noise in the training data.
3. Too many hypotheses were made for the model.
4. Increase the training data size if the model capacity increased

(1), (2), and (3) are all from the lecture.

(4): Although the model becomes more complicated, which may lead to overfitting, increasing the training data size would help prevent overfitting as long as we keep specific rate.

Q2-3: Are these statements true or false?

(A) Early stopping is practical technique to reduce training error.

(B) Pruning reduces the decision tree’s complexity but will improve its generalization ability.

1. True, True
2. True, False
3. False, True
4. False, False
5. Early stopping improves generalization ability (reducing generalization error). Somehow, it may cause the training error to be too low.
6. Pruning reduces certain sub-trees, thus reducing the complexity of a decision tree. By properly doing so, we can improve generalization ability by preventing overfitting.

Q2-4: Are these statements true or false?

(A) A validation set is used for model selection.

(B) Post-pruning is usually based on generalization error on the test set.

1. True, True
2. True, False
3. False, True
4. False, False
5. A validation set is usually used for evaluating the Generalization error while training so that we can choose the Best model of best generalization ability among many Candidate models. On the contrary, only test set used.
6. To estimate the generalization performance of the chosen best model, there are validation sets.
7. Post-pruning is usually based on the generalization error on the validation set.

Q3-1: Are these statements true or false?

(A) Least square used in regression trees.

(B) CART used to construct regression trees.

1. True, True
2. True, False
3. False, True
4. False, False

Conclusion

We measure the performance of tree by calculating the fraction of training instances that are correctly classified. Sentences can be true or false, and things can get complicated when answering them.

Therefore, to determine the following statements is true, if there is no noise, there is no overfitting. More training data may help prevent overfitting. Early stopping is practical technique to reduce training error. So, early stopping improves generalization ability increasing the training data size would help prevent overfitting as long as we keep specific rate.