Using the following training dataset, apply Naïve Bayes classification algorithm to find the class of an unknown sample X = < Rainy, Cool, High, False >
| S. No | Outlook | Temperature | Humidity | Windy | Play Golf/Class |
| 0 | Rainy | Hot | High | False | No |
| 1 | Rainy | Hot | High | True | No |
| 2 | Overcast | Hot | High | False | Yes |
| 3 | Sunny | Mild | High | False | Yes |
| 4 | Sunny | Cool | Normal | False | Yes |
| 5 | Sunny | Cool | Normal | True | No |
| 6 | Overcast | Cool | Normal | True | Yes |
| 7 | Rainy | Mild | High | False | No |
| 8 | Rainy | Cool | Normal | False | Yes |
| 9 | Sunny | Mild | Normal | False | Yes |
| 10 | Rainy | Mild | Normal | True | Yes |
| 11 | Overcast | Mild | High | True | Yes |
| 12 | Overcast | Hot | Normal | False | Yes |
| 13 | Sunny | Mild | High | True | Yes |
We will use the Naïve ___ ________ ___ ____ _________ ____ ____ __________ _____ ___ _____.
____ ___ ___ ______ ____ ____ ___ ____ _______ _______ _______ ________.
____.
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Compute the Linear Discriminant projection for the following two-dimensional dataset
and
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| 1 | 2 | 2 |
| 8 | 4 | |
| 7 | 6 | 5 |
| 2 | 8 | 1 |
| 4 | 3 | |
| 7 | 6 | 5 |
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