Week 5- Breast Cancer Detection
Hello everyone!
This is our fifth blog about our Machine Learning Course Project on Breast Cancer Detection. Last week we used the Support Vector Machine(SVM) classifier algorithm in our data set. This week we will use the Naïve Bayes algorithm.
Before we begin, let me briefly explain the Naïve Bayes algorithm.
Naïve Bayes
Naive Bayes algorithm is a classification technique based on Bayes’ Theorem with an assumption of independence among predictors. Simply put, the Naive Bayes classifier assumes that the existence of a particular property in a class is irrelevant to the existence of any other property.
Bayes Theorem:
P(A|B): The probability of A happening, given that B has occurred.
P(B|A): The probability of B happening, given that Ahas occurred.
P(A): The probability of A being true.
P(B): The probability of B being true.
Apply the Naive Bayes:
The Naïve Bayes classification method can be used in many ways, but it is important that what is classified here rather than how it is classified. Rather than the data type and what is happening here, it becomes more important how we establish a proportional relationship between these data.
Using the variables we have, we first generated probabilistic results with the train data. Then, in the test data, we obtained 2 probabilistic results according to the malignant and benign tumor status. Whichever of these possibilities outweighs, we estimate that the test data is most likely to exist.
The accuracy rate we received was 94,07729468599033 percent.
See you next week…
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