Basic Introduction to Statistical Learning — Set 1
By: Mohan Raj Raja
Tags: Regression, Supervised Learning, Machine Learning, Unsupervised Learning, Classification

This Article guides to approach the data set and basic mathematical relationship with Machine Learning Algorithms.

The content of this article is a result set of multiple online,books,workshops references. Any critics or comments, please feel free to reach out. This article has been trimmed to contain only the necessary information in order to give a precise and vivid gist to the readers.

Types of Learning : Supervised, Unsupervised and Re-reinforcement Learning.

Supervised Learning :In this Learning, the data set will have input and output. We must learn from the previous Inputs and Outputs and Predict the output for desired input.

Let’s imagine that you’re teaching a kid, about an Orange fruit by showing its image. In the future, the child should be able to recognize the fruit through its characteristics and features such as orange colored fruit, circle in shape, rough skin and its weight and more. It drills down to features and label, where the features are inputs and Orange acting as a label. Every Supervised data set will have features and labels as in below tabular column.

So Tomorrow when we say that “An object which is weight with 120 gm, Yellow Color and Oval Shape, Then what was that ?” the kid can say that “Its a Mango…”. Here the Kid is a Machine Learning model and we are training the model,we will talk in deep later.

Own data set — Classification Problems

Another example, say a rain data set having inputs features as Temperature : <some value>, Humidity : <some value> and output label as Rain in cm <some value>. The rain depends on these values. Refer below image.

Rain data set image taken from Kaggle — Regression

So in the above example, Fruit data set will falls in Classification problem and Rain data set will fall in Regression problem, any how will go deeper now. The Overall idea of Supervised Learning we already know the inputs and outputs of the data set, we learnt from them and we gonna predict the output based on future inputs.

Supervised Learning are of two types — Regression and Classification.

Regression : Its a statistical modelling technique. The objective of the Regression model is to use when “labels are values”, Values can be positive/negative numbers or continuous or quantitative. Let’s take a look at the Rain Data set shown earlier. ex : What will be the quantity of Rain in cm, if the Temperature=x and Humidity=y?

Classification : We can use this technique when the output label is categorical. Say, whether an object is a ball or a bat ?, will it rain today or not ?, fruit is an Orange or an Apple or a Mango ? and so on. Let’s call these outputs as classes. Our goal is to predict that if a particular feature belongs to these categories or classes.

Under Classification we have different types that is Binary Classification and Multi-Class classification.

Binary classification refers to simple output such as (Yes or No),(1 or 0),(Success or Failure),(Win or Lose) and so on whereas Multi-class refers when output is more than two classes such as (Yes or No or May Be), (Apple or Orange or Mango) ,(Dog or Cat or Fish) , Priority (1 or 2 or 3 or 4 or 5) and so on.

Summary of the Story : At this juncture of the article, you should have an idea for the question what machine learning does ?, how it works ? and what algorithm has to be chosen based on the output ?.

Next Series Link : Will be shared soon