[amp_mcq option1=”The polynomial degree” option2=”Whether we learn the weights by matrix inversion or gradient descent” option3=”The use of a constant-term” option4=”None of these” correct=”option1″]<\/p>\n
The correct answer is A. The polynomial degree<\/strong>.<\/p>\n The polynomial degree is the number of terms in the polynomial regression model. A higher polynomial degree will result in a more complex model that can fit the data more closely, but it is also more likely to overfit the data. A lower polynomial degree will result in a simpler model that is less likely to overfit the data, but it may not be able to fit the data as closely.<\/p>\n The choice of polynomial degree is a trade-off between under-fitting and over-fitting. Under-fitting occurs when the model is too simple and does not fit the data well. Over-fitting occurs when the model is too complex and fits the data too closely, resulting in poor performance on new data.<\/p>\n The other options are not as important in terms of the trade-off between under-fitting and over-fitting. Whether we learn the weights by matrix inversion or gradient descent is a technical detail that does not have a significant impact on the model’s performance. The use of a constant-term is a common practice in regression modeling, but it does not have a major impact on the trade-off between under-fitting and over-fitting.<\/p>\n","protected":false},"excerpt":{"rendered":" [amp_mcq option1=”The polynomial degree” option2=”Whether we learn the weights by matrix inversion or gradient descent” option3=”The use of a constant-term” option4=”None of these” correct=”option1″]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[729],"tags":[],"class_list":["post-45476","post","type-post","status-publish","format-standard","hentry","category-machine-learning","no-featured-image-padding"],"yoast_head":"\n