MLE, MAP, and Naive Bayes
Last updated
Last updated
Bayes' Theorem provides a way that we can calculate the posterior probability of a class/hypothesis/target P(c|x) given our prior knowledge of P(c), P(x) and P(x|c).
P(c|x): The posterior probability of class (c, target) given predictor (x, attributes).
P(c): The prior probability of class
P(x|c): The likelihood which is the probability of predictor given class
P(x): is the prior probability of predictor
Conjugate prior The prior P(c) is said to be conjugate to posterior P(x|c), if both P(c) and P(x|c) lies in the same family of distribution(e.g normal distribution)
Classification technique based on Bayes’ Theorem with an assumption of independence among predictors. It is called naive Bayes or idiot Bayes because it simplifies the calculation by assuming a particular feature in a class is unrelated to the presence of any other features.
Sample Problem: Players will play if weather is sunny. Is this statement is correct?
Step 1: Convert the data set into a frequency table
Step 2: Create Likelihood table by finding the probabilities like Overcast probability = 0.29 and probability of playing is 0.64
Step 3: Now, use Naive Bayesian equation to calculate the posterior probability for each class. The class with the highest posterior probability is the outcome of prediction.
Problem: Players will play if weather is sunny. Is this statement is correct?
We can solve it using above discussed method of posterior probability.
P(Yes | Sunny) = P( Sunny | Yes) * P(Yes) / P (Sunny)
Here we have P (Sunny |Yes) = 3/9 = 0.33, P(Sunny) = 5/14 = 0.36, P( Yes)= 9/14 = 0.64
Now, P (Yes | Sunny) = 0.33 * 0.64 / 0.36 = 0.60, which has higher probability.
MAP estimation is the value of the parameter that maximizes the entire posterior distribution (which is calculated using the likelihood). A MAP estimate is the mode/max of the posterior distribution. After calculating the posterior probability P(c|x) for a number of different classes/hypotheses (e.g: P(Yes | Sunny), P(No | Sunny) where 'Yes' and 'No' are two classes for the above example), you can select the hypothesis with the highest probability.
MLE of a parameter is the value of the parameter that maximizes the likelihood function of the unknown parameters given observed data.
Note: MLE = MAP if the prior distribution we were assuming was a constant.
Link: - https://machinelearningmastery.com/naive-bayes-for-machine-learning/ - http://blog.christianperone.com/2019/01/a-sane-introduction-to-maximum-likelihood-estimation-mle-and-maximum-a-posteriori-map/ - https://www.analyticsvidhya.com/blog/2017/09/naive-bayes-explained/
