Suppose we can afford taking a lot of samples, and for each sample, we fit the model and make a prediction $\backslash$hat$\{$f$\}($x$)$ about the signal f$($x$)$ at a given input x$.$ What does the bias$-$variance tradeoff phenomenon tell us about the predicted value $\backslash$hat$\{$f$\}($x$)?$

Group of answer choices

The simpler the model, the closer the average value of $\backslash$hat$\{$f$\}($x$)($accross all samples$)$ is to the true value f$($x$)$; however, the more likely will a single $\backslash$hat$\{$f$\}($x$)($i$.$e$.$ for a single sample$)$ be away from f$($x$).$

The more complex the model, the closer the average value of $\backslash$hat$\{$f$\}($x$)($accross all samples$)$ is to the true value f$($x$)$; however, the more likely will a single $\backslash$hat$\{$f$\}($x$)($i$.$e$.$ for a single sample$)$ be away from f$($x$).$