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.
Question
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.
Solution
The correct answer is: "In 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."
This is because in polynomial regression, increasing the power value (degree of the polynomial) can lead to a model that is too complex, which can result in overfitting. Overfitting is when the model fits the training data too closely and captures the noise along with the underlying pattern. This makes it perform poorly on new, unseen data. The high oscillations in the curve are a result of the model trying to hit every data point exactly, which leads to a curve that is over-flexible and not generalizable.
Similar Questions
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 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.
How does polynomial regression differ from linear regression? Polynomial regression can only have one predictor, while linear regression can have multiple Polynomial regression incorporates higher-degree terms of predictors Polynomial regression can only predict binary outcomes Polynomial regression is not affected by outliers
One of the pitfalls of fitting high-order polynomial trends is that such models can yield hghly inaccurate forecasts, particularly at long horizons.
Which of the following techniques can help prevent overfitting in regression models?
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