Why is boosting effective? Boosting takes a bunch of weak learners and creates a strong learner. But why is it so difficult to create a strong learner right from the beginning without using boosting techniques? And therefore remove the need for boosting.
 A: In most practical cases, one can do as you say. This is because most predictive models (such as the Gail model for breast cancer risk) only use a handful of extremely prognostic markers. That is to say we obtain predictors with very high recall having target parameter space of dimension $q \ll p$ (we do well with only a few predictors, and throw the rest away). 
But in absence of a "few good markers" and with an abundance of weak ones, no specialized technique exists outside of boosting which can give you low bias and good prediction. Why is that unique?


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*$p \gg n$: right off the bat, this excludes many traditional methods. With the parametric modeling approach, forward stepwise model selection, and even LASSO that means you can have at most $n$ predictors in the model and they will be biased. 

*$|w_j| < \epsilon$ , $j = \{1, 2, \ldots p\}$ for $\epsilon$ small. We might even state it in such a convergence criterion as $n ||w||_2 \rightarrow d$, $d$ finite.  i.e. we have really weak effects. Methods which are equivalent to tests of hypothesis in which $\mathcal{H}_0: w_j = 0$ will in general have too large a type II error rate for our purposes. This includes LASSO as I said.
