Question 2Which of the following statements about model errors is TRUE? 1 pointUnderfitting is characterized by lower errors in both training and test samples. Underfitting is characterized by higher errors in both training and test samples. Underfitting is characterized by higher errors in training samples and lower errors in test samples. Underfitting is characterized by lower errors in training samples and higher errors in test samples.
Question
Question 2Which of the following statements about model errors is TRUE? 1 pointUnderfitting is characterized by lower errors in both training and test samples. Underfitting is characterized by higher errors in both training and test samples. Underfitting is characterized by higher errors in training samples and lower errors in test samples. Underfitting is characterized by lower errors in training samples and higher errors in test samples.
Solution
The correct statement about model errors is: "Underfitting is characterized by higher errors in both training and test samples."
Here's why:
Underfitting occurs when a statistical model or machine learning algorithm cannot capture the underlying trend of the data. It happens when the model or the algorithm does not fit the data enough.
In terms of errors, underfitting is characterized by higher errors in both training and test samples. This is because the model fails to learn the data in the training phase (resulting in high training errors) and therefore also performs poorly on the test data (resulting in high test errors).
On the other hand, overfitting is characterized by low errors in training samples and high errors in test samples. This is because the model learns the training data too well, to the point that it captures the noise along with the underlying pattern. This makes it perform poorly on the test data.
Similar Questions
Question 3If a low-complexity model is underfitting during estimation, which of the following is MOST LIKELY true (holding the model constant) about K-fold cross-validation?1 pointK-fold cross-validation will still lead to underfitting, for any k.K-cross-validation with a small k will reduce or eliminate underfitting.K-fold cross-validation with a large k will reduce or eliminate underfitting.None of the above.
Which of the following is a characteristic of a model with high variance?Question 4AnswerA.It tends to underfit the training dataB.It performs well on the training data but poorly on unseen dataC.It has a low training error and a low test errorD.It has a high training error and a high test error
Which of the following statements about a linear regression model are correct?i) It is possible that a high R-square can result from outliersii) It is always true that the high the R-square, the better the linear regression model iii) Scatterplot can be helpful in identifying the existence of outliers in fitting a regression modeliv) Regression model cannot produce a range of predictions. v) A p-value higher than 0.5 suggests the estimated coefficient is statistically significant. Question 2Answera.i) and v)b.None of the abovec.i) and iii)d.iv) and v)e.i), ii) and iii)
Question 6Which of the following statements about datasets used in Machine Learning is NOT true?1 pointTesting data is data the model has never seen before and is used to evaluate how good the model isTraining subset is the data used to train the algorithm Training data is used to fine-tune algorithm’s parameters and evaluate how good the model isValidation data subset is used to validate results and fine-tune the algorithm's parameters
Question 6Which of the following statements about datasets used in Machine Learning is NOT true?1 pointTraining subset is the data used to train the algorithm Testing data is data the model has never seen before and is used to evaluate how good the model isTraining data is used to fine-tune algorithm’s parameters and evaluate how good the model isValidation data subset is used to validate results and fine-tune the algorithm's parameters
Upgrade your grade with Knowee
Get personalized homework help. Review tough concepts in more detail, or go deeper into your topic by exploring other relevant questions.