In my earlier post regarding Logistic-regression loss minimization we had seen that by changing the form of the loss function we can derive other machine learning models. It’s precisely this that we are going to talk about in this post.
Below is a table that shows a few loss functions…
We are going to use the same dataset we used in the Personal Cancer Diagnosis artile that was publised earlier
We will not be dealing with the exploratory data analysis but will straight go ahead with the machine learning model implementation of the dataset.
In Random Forests we have
Classify the given genetic variations/mutations based on evidence from text-based clinical literature.
Data: Thanks to Memorial Sloan Kettering Cancer Center (MSKCC) for curating this dataset a great help to humanity.
Let start with the assumptions that we need to make
Logisitc regression ==> simple Algorithm
Naive Bayes ==> No geometric intuition. Learnt it through Probabilistic technique
Logistic Regression can be learnt from three perspectives
This is a two class label(1 — client with payment difficulties, 0 — all other cases) classification problem.