You are working on a email spam filtering assignment, while working on this you find there is new word e.g. HadoopExam comes in email, and in your solutions you never come across this word before…

The correct answer is B. Laplace Smoothing. Laplace smoothing is a technique for parameter estimation which accounts for unobserved events. It is more robust and will not fail completely when data that has never been observed in training shows up.

Machine Learning Algorithms

Question

You are working on a email spam filtering assignment, while working on this you find there is new word e.g. HadoopExam comes in email, and in your solutions you never come across this word before, hence probability of this words is coming in either email could be zero. So which of the following algorithm can help you to avoid zero probability?

Options

ANaive Bayes
BLaplace Smoothing
CLogistic Regression
DAll of the above

How the community answered

(30 responses)

A
3% (1)
B
83% (25)
C
7% (2)
D
7% (2)

Explanation

Laplace smoothing is a technique for parameter estimation which accounts for unobserved events. It is more robust and will not fail completely when data that has never been observed in training shows up.

Topics

#Laplace smoothing#Naive Bayes#zero probability#spam filtering

Community Discussion

No community discussion yet for this question.

Full DATABRICKS-CERTIFIED-PROFESSIONAL-DATA-SCIENTIST Practice