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Туре? Patrons? French Italian Thai Burger Nore Some 136 8 10 No Yes Hungry? No Yes 4 12 2 10 (a) (b) Figure 18.4 Splitting the examples by testing on attributes. At each node we show the positive (light boxes) and negative (dark boxes) examples remaining. (a) Splitting on Type brings us no nearer to distinguishing between positive and negative examples (b) Splitting on Patrons does a good job of separating positive and negative examples. After splitting on Patrons, Hungry is a fairly good second test. bination of attribute values, and we retum a default value calculated from the plurality classification of all the examples that were used in constructing the node's parent. These are passed along in the variable parent.ezamples. 4. If there are no attributes left, but both positive and negative examples, it means that these examples have exactly the same description, but different classifications. This can happen because there is an error or noise in the data; because the domain is nondeter- ministic; or because we can't obscrve an attribute that would distinguish the examples. The best we can do is retum the plurality classification of the remaining examples. The DECISION-TREE-LEARNING algorithm is shown in Figure 18.5. Note that the set of examples is crucial for constructing the tree, but nowhere do the examples appear in the tree itself. A tree consists of just tests on attributes in the interior nodes, values of attributes on the branches, and output values on the leaf nodes. The details of the IMPORTANCE function are given in Section 18.3.4. The output of the learning algorithm on our sample training set is shown in Figure 18.6. The tree is clearly different from the original tree shown in Figure 18.2. One might conclude that the leaming algorithm is not doing a very good job of learming the correct function. This would be the wrong conclusion to draw, however. The learning algorithm looks at the examples, not at the correct function, and in fact, its hypothesis (see Figure 18.6) not only is consistent with all the examples, but is considerably simpler than the original tree! The leaming algorithm has no reason to include tests for Raining and Reservation, because it can classify all the examples without them. It has also detected an interesting and previously unsuspected pattern: the first author will wait for Thai food on weekends. It is also bound to make some mistakes for cases where it has seen no examples. For example, it has never seen a case where the wait is 0-10 minutes but the restaurant is full.

Туре? Patrons? French Italian Thai Burger Nore Some 136 8 10 No Yes Hungry? No Yes 4 12 2 10 (a) (b) Figure 18.4 Splitting the examples by testing on attributes. At each node we show the positive (light boxes) and negative (dark boxes) examples remaining. (a) Splitting on Type brings us no nearer to distinguishing between positive and negative examples (b) Splitting on Patrons does a good job of separating positive and negative examples. After splitting on Patrons, Hungry is a fairly good second test. bination of attribute values, and we retum a default value calculated from the plurality classification of all the examples that were used in constructing the node's parent. These are passed along in the variable parent.ezamples. 4. If there are no attributes left, but both positive and negative examples, it means that these examples have exactly the same description, but different classifications. This can happen because there is an error or noise in the data; because the domain is nondeter- ministic; or because we can't obscrve an attribute that would distinguish the examples. The best we can do is retum the plurality classification of the remaining examples. The DECISION-TREE-LEARNING algorithm is shown in Figure 18.5. Note that the set of examples is crucial for constructing the tree, but nowhere do the examples appear in the tree itself. A tree consists of just tests on attributes in the interior nodes, values of attributes on the branches, and output values on the leaf nodes. The details of the IMPORTANCE function are given in Section 18.3.4. The output of the learning algorithm on our sample training set is shown in Figure 18.6. The tree is clearly different from the original tree shown in Figure 18.2. One might conclude that the leaming algorithm is not doing a very good job of learming the correct function. This would be the wrong conclusion to draw, however. The learning algorithm looks at the examples, not at the correct function, and in fact, its hypothesis (see Figure 18.6) not only is consistent with all the examples, but is considerably simpler than the original tree! The leaming algorithm has no reason to include tests for Raining and Reservation, because it can classify all the examples without them. It has also detected an interesting and previously unsuspected pattern: the first author will wait for Thai food on weekends. It is also bound to make some mistakes for cases where it has seen no examples. For example, it has never seen a case where the wait is 0-10 minutes but the restaurant is full.

Computer Networking: A Top-Down Approach (7th Edition)
Computer Networking: A Top-Down Approach (7th Edition)
7th Edition
ISBN:9780133594140
Author:James Kurose, Keith Ross
Publisher:James Kurose, Keith Ross
Chapter1: Computer Networks And The Internet
Section: Chapter Questions
Problem R1RQ: What is the difference between a host and an end system? List several different types of end...
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12 9 10 11 25 11 | Туре? Patrons? French Italian Thai Burger None Some Full 1 4 8 3 12 13 E 8 10 2 11 10 Hungry? No Yes No Yes 4 12 2 10 (a) (b) Figure 18.4 Splitting the examples by testing on attributes. At each node we show the positive (light boxes) and negative (dark boxes) examples remaining. (a) Splitting on Type brings us no nearer to distinguishing between positive and negative examples. (b) Splitting on Patrons does a good job of separating positive and negative examples. After splitting on Patrons, Hungry is a fairly good second test. bination of attribute values, and we retum a default value calculated from the plurality classification of all the examples that were used in constructing the node's parent. These are passed along in the variable parenteramples. 4. If there are no attributes left, but both positive and negative examples, it means that these examples have exactly the same description, but different classifications. This can happen because there is an error or noise in the data; because the domain is nondeter- ministic; or because we can't observe an attribute that would distinguish the examples. The best we can do is retum the plurality classification of the remaining examples. The DECISION-TREE-LEARNING algorithm is shown in Figure 18.5. Note that the set of examples is crucial for constructing the tree, but nowhere do the examples appear in the tree itself. A tree consists of just tests on attributes in the interior nodes, values of attributes on the branches, and output values on the leaf nodes. The details of the IMPORTANCE function are given in Section 18.3.4. The output of the learning algorithm on our sample training set is shown in Figure 18.6. The tree is clearly different from the original tree shown in Figure 18.2. One might conclude that the leaming algorithm is not doing a very good job of learning the correct function. This would be the wrong conclusion to draw, however. The learning algorithm looks at the examples, not at the correct function, and in fact, its hypothesis (see Figure 18.6) not only is consistent with all the examples, but is considerably simpler than the original tree! The learning algorithm has no reason to include tests for Raining and Reservation, because it can classify all the examples without them. It has also detected an interesting and previously unsuspected pattern: the first author will wait for Thai food on weekends. It is also bound to make some mistakes for cases where it has seen no examples. For example, it has never seen a case where the wait is 0-10 minutes but the restaurant is full.
Transcribed Image Text:12 9 10 11 25 11 | Туре? Patrons? French Italian Thai Burger None Some Full 1 4 8 3 12 13 E 8 10 2 11 10 Hungry? No Yes No Yes 4 12 2 10 (a) (b) Figure 18.4 Splitting the examples by testing on attributes. At each node we show the positive (light boxes) and negative (dark boxes) examples remaining. (a) Splitting on Type brings us no nearer to distinguishing between positive and negative examples. (b) Splitting on Patrons does a good job of separating positive and negative examples. After splitting on Patrons, Hungry is a fairly good second test. bination of attribute values, and we retum a default value calculated from the plurality classification of all the examples that were used in constructing the node's parent. These are passed along in the variable parenteramples. 4. If there are no attributes left, but both positive and negative examples, it means that these examples have exactly the same description, but different classifications. This can happen because there is an error or noise in the data; because the domain is nondeter- ministic; or because we can't observe an attribute that would distinguish the examples. The best we can do is retum the plurality classification of the remaining examples. The DECISION-TREE-LEARNING algorithm is shown in Figure 18.5. Note that the set of examples is crucial for constructing the tree, but nowhere do the examples appear in the tree itself. A tree consists of just tests on attributes in the interior nodes, values of attributes on the branches, and output values on the leaf nodes. The details of the IMPORTANCE function are given in Section 18.3.4. The output of the learning algorithm on our sample training set is shown in Figure 18.6. The tree is clearly different from the original tree shown in Figure 18.2. One might conclude that the leaming algorithm is not doing a very good job of learning the correct function. This would be the wrong conclusion to draw, however. The learning algorithm looks at the examples, not at the correct function, and in fact, its hypothesis (see Figure 18.6) not only is consistent with all the examples, but is considerably simpler than the original tree! The learning algorithm has no reason to include tests for Raining and Reservation, because it can classify all the examples without them. It has also detected an interesting and previously unsuspected pattern: the first author will wait for Thai food on weekends. It is also bound to make some mistakes for cases where it has seen no examples. For example, it has never seen a case where the wait is 0-10 minutes but the restaurant is full.
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