**(Rule-Based Classifier)**Consider the decision tree- Convert the decision tree into a set of classification rule sets
- Are the rules mutually exclusive?
- Is the rule set exhaustive?
- Is ordering needed for this set of rules?
- Do you need a default class for the rule set?

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**(Nearest Neighbor Classifier)**The table below lists a dataset that was used to create a nearest neighbor model that predicts whether it will be a good day to go surfing

ID | Wave Size (Ft) | Wave Period (secs) | Wind Speed (mph) | Good Surf |

1 | 6 | 15 | 5 | Yes |

2 | 1 | 6 | 9 | No |

3 | 7 | 10 | 4 | Yes |

4 | 7 | 12 | 3 | Yes |

5 | 2 | 2 | 10 | No |

6 | 10 | 2 | 20 | No |

Assuming that the model uses **Euclidean distance **to find the **k-nearest neighbor**, what prediction will the model return for the following query instances when **k = 1, 3**?

Wave Size (Ft) | Wave Period (secs) | Wind Speed (mph) | Good Surf |

5 | 6 | 7 | ? |

**(Naïve Bayes Classifier)**The table below gives details of symptoms that patients presented and whether they were suffering from

ID | Headache | Fever | Vomiting | Meningitis |

1 | True | True | False | False |

2 | False | True | False | False |

3 | True | False | True | False |

4 | True | False | True | False |

5 | False | True | False | True |

6 | True | False | True | False |

7 | True | False | True | False |

8 | True | False | True | True |

9 | False | True | False | False |

10 | True | False | True | True |

Headache | Fever | Vomit | Meningitis |

False | False | True | ? |

Using a **naïve Bayes classifier **to determine whether a patient with the above symptoms have meningitis.

**(Bayesian Network)**Given the Bayesian Network below, what is the joint probability distribution P(Exercise = Yes, Diet = Healthy, Heart Disease = No, Chest Pain = No, Blood Pressure = low)?

**(Perceptron)**Given the perceptron Y = sign(0.4X1+0.3X2+0.7X3-0.5). What is the prediction when X1=0.5, X2=-0.5, X3=0.8?

**(SVM)**Given w = (0.4, 0.3, 0.7) and b = -0.5.- What is the margin of the SVM?
- What is the prediction when X1=-0.5, X2=-0.5, X3=0.8? (2 points)

**(Performance Evaluations)**Given the confusion matrix

- Calculate the accuracy
- Calculate the F-measure