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…

Random forest

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

  • Decision…

Problem statement :

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.

Business Objective and Constraints

  • There is no Latency requirement
  • High accuracy is required as we are dealing with human lives
  • Errors are…

Let start with the assumptions that we need to make

  • The class label Y takes only two outcomes+1, 0 like a coin toss and hence can be thought of as a Benoulli random variable. The first big assumption is that the class label Y has a Bernoulli distribution.
  • We have…

Logisitc regression ==> simple Algorithm

Naive Bayes ==> No geometric intuition. Learnt it through Probabilistic technique

Logistic Regression can be learnt from three perspectives

  • Geometric intuition
  • probabilistic approach which involves dense mathematics
  • Loss minimization framework


  • Classes are linearly or almost linearly seperable plane

Seq 2 Seq Models


  • Google Translate
  • Auto Reply
  • Auto Suggestion

Seminal paper — ILya Sutskever

Machine Translation ==> Seq to Seq Models

Janardhanan a r

In the making Machine Learner programmer music lover

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