Question

Can you please solve the question which is vital for me, clealy?

and d) By regularizing w_n

We try to solve the binary classification task ilustrated in the below figure with a simple linear log istic regression model Notice that the training data can be separated with zero training error with a linear separator. Consider training regularized linear logistic regression models where we try to maximize for very large . The regularization penalties used in penalized conditional lag likelihood estimation are -Cu, where(0,1.2). In other words, only one of the parameters is regularized in each case. Given the training data in the figure above, how does the training error change with regularization of each parameter ω,? State whether the training error increases or stays the same (zero) for each wj for very large C. Provide a brief justification for each of your (a) By regularizing w2 (b) By regularizing (c) By regularizing w

Answer 1

Given 1 O 0 1 tinegt [ Considun traini models where we try to rm μ mee. cato) ulere

Yeguiaries o decsuanu Yea tin causene training O C orć Some part or b. P ctasses vemain on the Samt s

trainin aul or ia te trainen Since both Ro s can remains inetind eror re he