The table below contains 11 training examples with three attributes each:
| ID# | Texture | Temperature | Size | Classification |
| 1 | smooth | cold | large | yes |
| 2 | smooth | cold | small | no |
| 3 | smooth | cool | large | yes |
| 4 | smooth | cool | small | yes |
| 5 | smooth | hot | small | yes |
| 6 | wavy | cold | medium | no |
| 7 | wavy | hot | large | yes |
| 8 | rough | cold | large | no |
| 9 | rough | cool | large | yes |
| 10 | rough | hot | small | no |
| 11 | rough | warm | medium | yes |
Use the "current best hypothesis" technique (discussed in session 25 and in section 19.1 of your textbook) to develop a model for the data as each training example is added to your knowledge domain. At each step in the process your model should be consistent for that training example AS WELL, as all prior examples.
| After training example # | Starting Model | Consistent with this example? | If not, false positive or false negative | If not, adjust the model to handle the new information |
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| 11 |
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