Overfitting occurs when a model:Question 7AnswerA.Has high bias and low varianceB.Has low bias and high varianceC. Performs well on unseen dataD. Fails to capture the underlying patterns in the data

Overfitting occurs when a model performs well on training data but poorly on unseen data.Group of answer choicesTrueFalse

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

A machine learning model is trained to predict customer churn for a telecom company. The model achieves high accuracy during training but performs poorly when applied to new, unseen data. What could be the most likely cause of this issue?a)Inappropriate choice of evaluation metricb)Insufficient training datac)Underfittingd)Overfitting

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

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.