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Tree recursive binary partitioning

To keep the description of the regions simple, binary decision trees with a root, nodes, and leaves, the R, s, are favored. Each region is split into two further regions over and over again termed a recursive binary partitioning. The decision for the binary split is based on a constant, s, as shown in Figure 5.34b. [Pg.201]

Another nonparametric regression method is CART (classification and regression trees). The basic concepts were outlined in Discriminant Analysis Section about tree-based classification. We remember from that chapter that CART is a recursive binary partition method based on a simple model constant for each region. If the residual sums of squares of responses is minimized. [Pg.267]

Figure 5.34 Partitioning in CART The panel (a) demonstrates the partitioning of a two-dimensional feature space into four regions by recursive binary splitting. In the panel (b), the resulting tree is shown. Figure 5.34 Partitioning in CART The panel (a) demonstrates the partitioning of a two-dimensional feature space into four regions by recursive binary splitting. In the panel (b), the resulting tree is shown.

See other pages where Tree recursive binary partitioning is mentioned: [Pg.721]    [Pg.705]    [Pg.42]    [Pg.91]    [Pg.361]    [Pg.42]    [Pg.95]    [Pg.95]    [Pg.308]    [Pg.130]    [Pg.276]   
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