Which of the following comments about polynomial models is FALSE?Group of answer choicesOverfitting may occur if we have too many polynomial terms in the model.For prediction, it is always preferable to include as many polynomial terms as possible.An overfitted model may have a R2 statistic of 100%, but would still be useless for prediction.If we observe a fan effect in our initial residual plot, carrying out a log transformation may fix this.
Question
Which of the following comments about polynomial models is FALSE?Group of answer choicesOverfitting may occur if we have too many polynomial terms in the model.For prediction, it is always preferable to include as many polynomial terms as possible.An overfitted model may have a R2 statistic of 100%, but would still be useless for prediction.If we observe a fan effect in our initial residual plot, carrying out a log transformation may fix this.
Solution
The false statement is: "For prediction, it is always preferable to include as many polynomial terms as possible."
Including too many polynomial terms can lead to overfitting, where the model becomes too complex and starts to capture the noise in the data rather than the underlying pattern. This can result in poor predictive performance when the model is applied to new data.
Similar Questions
Question 18Which statement is true about overfitting?1 pointThe model is too flexible and fits the noise rather than the function.If the model is noisy, you need a low-order polynomial so you don’t overfit the data.The higher the order of the polynomial, the less overfitting occurs.If a model is overfit with the training data it will also overfit the testing data.
Which of the following may be the cause for the occurrence of curves lead to overfitting in polynomial regression?Select an option Clear ResponseIn order to obtain a low error rate, increasing the power value results in high oscillations in the curve which leads to the shape of the curve that are over-flexible.In order to obtain a low error rate, decreasing the power value results in high oscillations in the curve which leads to the shape of the curve that are over-flexible.In order to obtain a high error rate, increasing the power value results in high oscillations in the curve which leads to the shape of the curve that are over-flexible.In order to obtain a high error rate, decreasing the power value results in high oscillations in the curve which leads to the shape of the curve that are over-flexible.
Overfitting occurs when a model performs well on training data but poorly on unseen data.Group of answer choicesTrueFalse
One of the pitfalls of fitting high-order polynomial trends is that such models can yield hghly inaccurate forecasts, particularly at long horizons.Group of answer choicesTrueFalse
Polynomial regression can model nonlinear relationships by transforming the features.Group of answer choicesTrueFalse
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