Question

1. Consider the following Training Data Set: • Apply the Nave Bayesian Classifier to this data set and compute the probability score for P(y = 1|X) for X = (1,0,0) Show your work Training Data Set X1 X2 X3 Y 1 1 1 0 1 1 0 0 0 0 0 0 0 1 0 1 1 0 1 1 0 1 1 1 2. List some prominent use cases of the Nave Bayesian Classifier. 3. What gives the Nave Bayesian Classifier the advantage of being computationally inexpensive? 4. Why should we use log-likelihoods rather than pure probability values in the Nave Bayesian Classifier?

          1. Consider the following Training Data Set:
• Apply the Nave Bayesian Classifier to this data set and compute the probability score for P(y = 1|X) for X = (1,0,0)
Show your work
Training Data Set
X1 X2 X3 Y
1 1 1 0
1 1 0 0
0 0 0 0
0 1 0 1
1 0 1 1
0 1 1 1
2. List some prominent use cases of the Nave Bayesian Classifier.
3. What gives the Nave Bayesian Classifier the advantage of being computationally inexpensive?
4. Why should we use log-likelihoods rather than pure probability values in the Nave Bayesian Classifier?

1. Consider the following Training Data Set:
• Apply the Nave Bayesian Classifier to this data set and compute the probability score for P(y = 1|X) for X = (1,0,0)
Show your work
Training Data Set
X1 X2 X3 Y
1 1 1 0
1 1 0 0
0 0 0 0
0 1 0 1
1 0 1 1
0 1 1 1
2. List some prominent use cases of the Nave Bayesian Classifier.
3. What gives the Nave Bayesian Classifier the advantage of being computationally inexpensive?
4. Why should we use log-likelihoods rather than pure probability values in the Nave Bayesian Classifier?

Added by Zoe G.

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