Question

We have the following training dataset: X (exponential) Y (normal) Z (categorical) L (target class) 0.022 0.470 Up No 0.533 1.044 Up Down No Yes 0.413 -0.587 0.181 -1.461 Down No 0.365 -1.170 Down Yes 2.568 -0.547 Up Up No Yes 0.081 -0.678 0.463 0.313 Up No 0.511 2.080 Up No 0.303 -0.760 Down Yes In this dataset, X, Y, and Z are the features and the classes are shown in column L. X is exponentially distributed, Y is normally distributed, and Z is a categorical variable. (a) By calculating the probabilities, train a Naive Bayes classifier. (15 points) (b) Using your classifier from part (a), predict the class of the following input: (5 points) X = 0.07, Y = 0, and Z = Down

          We have the following training dataset:
X (exponential) Y (normal)
Z (categorical) L (target class)
0.022
0.470
Up
No
0.533
1.044
Up Down
No Yes
0.413
-0.587
0.181
-1.461
Down
No
0.365
-1.170
Down
Yes
2.568
-0.547
Up Up
No Yes
0.081
-0.678
0.463
0.313
Up
No
0.511
2.080
Up
No
0.303
-0.760
Down
Yes

In this dataset, X, Y, and Z are the features and the classes are shown in column L. X is exponentially distributed, Y is normally distributed, and Z is a categorical variable.

(a) By calculating the probabilities, train a Naive Bayes classifier. (15 points)

(b) Using your classifier from part (a), predict the class of the following input: (5 points)
X = 0.07, Y = 0, and Z = Down

we have the following training dataset x exponential y normal z categorical l target class 0022 0470 up no 0533 1044 up down no yes 0413 0587 0181 1461 down no 0365 1170 down yes 2568 0547 u 62664

Added by Maria N.

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