# Cancer Diagnosis — Stochastic Gradient Descent Algorithms

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…

# Cancer Diagnosis — 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…

# Personal Cancer Daignosis

## Problem statement :

Classify the given genetic variations/mutations based on evidence from text-based clinical literature.

## Data

Data: Thanks to Memorial Sloan Kettering Cancer Center (MSKCC) for curating this dataset a great help to humanity.

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

# Logistic Regression — probabilistic interpretation

• 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…

# Logistic Regression

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

# Assumption

• Classes are linearly or almost linearly seperable plane

# Applications

• Auto Suggestion

Seminal paper — ILya Sutskever

Machine Translation ==> Seq to Seq Models

# Objective

This is a two class label(1 — client with payment difficulties, 0 — all other cases) classification problem.

1) Introduction

3) Mapping to ML problem

4) Introduction to Datasets

5) Existing approaches

6) First Cut Approach

7) Exploratory Data Analysis (EDA)

8) Feature engineering

9) Model Explanation ## Janardhanan a r

In the making Machine Learner programmer music lover