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Java Universal Product Code version E Basic Models for Supervised Learning in Visual Studio .NET Encoding PDF-417 2d barcode in Visual Studio .NET Basic Models for Supervised Learning

7.3. Basic Models for Supervised Learning Using Barcode generation for Java Control to generate, create UPC-E image in Java applications.Java UPC - E0 for Java predict rea Java UPC E d only if the author is known. This decision tree can correctly classify all examples in Figure 7.1 (page 289).

The tree on the right makes probabilistic predictions when the length is short. In this case, it predicts reads with probability 0.82 and so skips with probability 0.

18.. gs1 databar A determini stic decision tree, in which all of the leaves are classes, can be mapped into a set of rules, with each leaf of the tree corresponding to a rule. The example has the classi cation at the leaf if all of the conditions on the path from the root to the leaf are true. Example 7.

6 The leftmost decision tree of Figure 7.4 can be represented as the following rules:. skips lon GS1 - 12 for Java g. reads short new. reads short followUp known.

skips short followUp unknown. With negation as failure (page 194), the rules for either skips or reads can be omitted, and the other can be inferred from the negation..

To use deci sion trees as a target representation, there are a number of questions that arise:. Given som GS1 - 12 for Java e training examples, what decision tree should be generated Because a decision tree can represent any function of the input features, the bias that is necessary to learn is incorporated into the preference of one decision tree over another. One proposal is to prefer the smallest tree that is consistent with the data, which could mean the tree with the least depth or the tree with the fewest nodes. Which decision trees are the best predictors of unseen data is an empirical question.

How should an agent go about building a decision tree One way is to search the space of decision trees for the smallest decision tree that ts the data. Unfortunately the space of decision trees is enormous (see Exercise 7.7 (page 344)).

A practical solution is to carry out a local search on the space of decision trees, with the goal of minimizing the error. This is the idea behind the algorithm described below..

Searching for a Good Decision Tree A decision UCC - 12 for Java tree can be incrementally built from the top down by recursively selecting a feature to split on and partitioning the training examples with respect to that feature. In Figure 7.5 (on the next page), the procedure DecisionTreeLearner learns a decision tree for binary attributes.

The decisions regarding when to stop and which feature to split on are left unde ned. The procedure DecisionTreeClassify takes in a decision tree produced by the learner and makes predictions for a new example..

7. Learning: Overview and Supervised Learning 1: 2: 3: 4: UPC-E Supplement 2 for Java 5: 6: 7: 8: 9: 10: 11: 12: 13: 14: 15: 16: 17: 18: 19: 20: 21: 22: 23: 24: 25: 26: 27: 28: 29: 30: 31: 32: 33: 34:. procedure D ecisionTreeLearner(X, Y, E) Inputs X: set of input features, X = {X1 , . . .

, Xn } Y: target feature E: set of training examples Output decision tree if stopping criterion is true then return pointEstimate(Y, E) else Select feature Xi X, with domain {v1 , v2 } let E1 = {e E : val(e, Xi )=v1 } let T1 = DecisionTreeLearner(X \ {Xi }, Y, E1 ) let E2 = {e E : val(e, Xi )=v2 } let T2 = DecisionTreeLearner(X \ {Xi }, Y, E2 ) return Xi =v1 , T1 , T2 procedure DecisionTreeClassify(e, X, Y, DT) Inputs X: set of input features, X = {X1 , . . .

, Xn } Y: target feature e: example to classify DT: decision tree Output prediction on Y for example e Local S subtree of DT S := DT while S is an internal node of the form Xi =v, T1 , T2 do if val(e, Xi )=v then S : = T1 else S : = T2 return S Figure 7.5: Decision tree learning and classi cation for binary features. The algorit Java UCC - 12 hm DecisionTreeLearner builds a decision tree from the top down as follows: The input to the algorithm is a set of input features, a target feature, and a set of examples. The learner rst tests if some stopping criterion is true. If the stopping criterion is true, it returns a point estimate (page 288) for Y, which is either a value for Y or a probability distribution over the values for Y.

If the stopping criterion is not true, the learner selects a feature Xi to split.
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