## Decision Tree

Decision Tree is a graphical representation that identifies ways to split a data set based on different conditions. It is one of the most widely used and practical methods for supervised learning used for both classification and regression tasks. Decision Tree is a tree-like structure or model of decisions with Read more…

## Regression Techniques

Ques- What all different types of Regression Techniques and explain each one of it. Answer: There are seven regression techniques exist and below are the names of it.

## Entropy

Entropy: It defines the randomness in the data. It helps to find out the root node,intermediate nodes and leaf node to develop the decision tree It is just a metric which measures the impurity. It reaches its minimum (zero) when all cases in the node fall into a single target Read more…

## Gini Impurity

Gini Impurity: It is a measure of how often a randomly chosen element from the set would be incorrectly labelled. It helps to find out the root node,intermediate nodes and leaf node to develop the decision tree It is used by the CART (classification and regression tree) algorithm for classification Read more…

## Maths behind Linear Regression

As we have already discussed in overview section that simple linear regression aims at finding the best fit line.Consider the below plot between experience and salary. In order to find the best lines to cover almost points in the above scatter plot, we use a metric called “Sum of Squared Read more…

## Correlation

In many practical scenarios, we might come across the situation where observations are available on two or more variables like heights and weights of the person ,expenditure on advertisement and sales revenue, tweet likes and popularity index of person etc. A natural question arises in mind that is there any Read more…

## Simple Linear Regression

In last article we learned about the types of machine learning algorithm. Lets us explore one of those techniques – Simple Linear Regression. In simple linear regression, we need to predict value of one dependent variable on the basis of given independent variable. Throughout this blog, we will use below Read more…

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