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 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 3What is overfitting in machine learning?a) Overfitting occurs when a model has high complexity and captures both information and noise in the training data.b) Overfitting occurs when a model has poor performance on the training data.c) Overfitting is indicated when a model has good performance on the training dataset but relatively poor performance on the testing dataset.d) Overfitting occurs when a model has good performance on the test data.Answer choicesSelect only one optionREVISITa onlya & da & ca, c & d

Question 7What method can you use to minimize overfitting of a machine learning model?1 pointIncrease the variance of your training data.Tune the hyperparameters of your model using cross-validation.Choose the hyperparameters that maximize goodness of fit on your training data.Decrease the variance of your test 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.

How does overfitting affect the performance of a machine learning model? It improves generalization to unseen data It increases bias and decreases variance It decreases generalization to unseen data It has no effect on the model's performance