Good Question.
From your Question i think what you refer to as pruning is a model-building step performed ex ante--ie, to limit the inputs available to the algorithm to build the model. The term pruning when used in Machine Learning refers to something different--an ex post step, after model construction and that operates upon the model itself and not on the available inputs. (There could be a second meaning in the ML domain, for the term pruning. of, but i m not aware of it.) In other words, pruning is indeed literally a technique to "limit its chain of deduction" as you put it, but it does so ex post, by excision of components of a complete (working) model, and not by limiting the inputs used to create that model.
On the other hand, isolating or limiting the inputs available for model construction--which is what i think you might have had in mind--is indeed a key Machine Learning theme; it s clearly a factor responsible for the superior performance of many of the more recent ML algorithms--for instance, Support Vector Machines (the insight that underlies SVM is construction of the maximum-margin hyperplane from only a small subset of the data, i.e, the support vectors ), and Multi-Adaptive Regression Splines (a regression technique in which no attempt is made to fit the data by "drawing a single continuous curve through it", instead, discrete section of the data are fit, one by one, using a bounded linear equation for each portion, ie., the splines , so the predicate step of optimal partitioning of the data is obviously the crux of this algorithm).
What problem is solving by pruning?
At least w/r/t specific ML algorithms i have actually coded and used--Decision Trees, MARS, and Neural Networks--pruning is performed on an initially over-fit model (a model that fits the training data so closely that it is unable to generalize (accurately predict new instances). In each instance, pruning involves removing marginal nodes (DT, NN) or terms in the regression equation (MARS) one by one.
Second, why is pruning necessary/desirable?
Isn t it better to just accurately set the convergence/splitting criteria? That won t always help. Pruning works from "the bottom up"; the model is constructed from the top down, so tuning the model (to achieve the same benefit as pruning) eliminates not just one or more decision nodes but also the child nodes that (like trimming a tree closer to the trunk). So eliminating a marginal node might also eliminate one or more strong nodes subordinate to that marginal node--but the modeler would never know that because his/her tuning eliminated further node creation at that marginal node. Pruning works from the other direction--from the most subordinate (lowest-level) child nodes upward in the direction of the root node.
