Artificial Intelligence for Dummies by Mueller John Paul;Massaron Luca; & Luca Massaron

Author:Mueller, John Paul;Massaron, Luca; & Luca Massaron [Mueller, John Paul & Massaron, Luca]
Language: eng
Format: epub
Publisher: John Wiley & Sons, Incorporated
Published: 2021-10-29T00:00:00+00:00

Pruning overgrown trees

Even though the play tennis dataset in the previous section illustrates the nuts and bolts of a decision tree, it has little probabilistic appeal because it proposes a set of deterministic actions (it has no conflicting instructions). Training with real data usually doesn’t feature such sharp rules, thereby providing room for ambiguity and the likelihood of the hoped for outcome.

Decision trees have more variability in their estimations because of the noise that they obtain from data during the learning process (an effect of overfitting). To overfit the data less, the example specifies that the minimum split has to involve at least five examples. Because the terminal leaves are numerically larger, the confidence that the tree is picking the correct signal increases because the evidence quantity is higher. Also, it prunes the tree. Pruning happens when the tree is fully grown.

Starting from the leaves, the example prunes the tree of branches, showing little improvement in the reduction of information gain. By initially letting the tree expand, branches with little improvement are tolerated because they can unlock more interesting branches and leaves. Retracing from leaves to root and keeping only branches that have some predictive value reduces the variance of the model, making the resulting rules restrained.

For a decision tree, pruning is just like brainstorming. First, the algorithm generates all possible ramifications of the tree (as you do with ideas in a brainstorming session). Second, when the brainstorming concludes, only feasible ideas are retained, and the algorithm keeps only what really works.

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