## What is the difference between Precision-Recall Curve vs ROC-AUC curve?

In Machine Learning, it is very important to have good understanding of different performance metrics. And it is even more important to know when to use which one to correctly explain the model performance. In classification problems more specific to binary classification, you can not conclude your model without plotting Precision-Recall curve and ROC-AUC curve. In this post, will learn what is the main difference between Precision-Recall curve and ROC-AUC curve and when to use which one.

## Decision Tree for Regression Models in Machine Learning

The ID3 algorithm can be used to construct a decision tree for regression type problems by replacing Information Gain with Standard Deviation Reduction – SDR
A decision tree is built top down from a root node and involves partitioning the data into subsets that contain instances with similar values mean homogeneous data.
Here, standard deviation is used to calculate the homogeneity of a numerical sample (target variable).

## What is the Significance of ROC AUC Curve?

ROC AUC curve helps you to determine the threshold of binary classification problems in machine learning. In Machine Learning classification problems are based on the probability value and its not always correct to have the threshold as 0.5. It depends on the type and domain of the problem. For example in a legal case you don’t want the false positive to be high or it should be at least as possible. so the threshold in this case would be very high. the term AUC that is Area under curve tells us the model goodness of fit. It is used to do the comparative analysis between different classifiers and identify which one is performing good.

## What is Gini and Gini Index in Decision Tree

In this video post I have explained about the below ML FAQ:
1. What is Gini
2. What is Gini Index
3. How to calculate Gini and Gini Index
4. How Gini Index helps to decide the Parent and Decision nodes in Decision Tree

## What is Entropy and Information Gain in Decision Tree?

In this video I will be explaining about the following ML questions:
1. How to Interpret Decision Tree
2. How splitting happens in the decision tree.
3. What is Entropy?
4. What in Information Gain?
5. How Information gain helps to decide the parent node and further node split?

## 7 Fun Comic Illustrations That Best Describe Machine Learning

When are you looking for the best way to express yourself without speaking, which technique comes to your mind? Yes, that’s right, it is an illustration. The illustration is one of the best ways to express a feeling or describe something. This technique has been started by our ancestors in the primitive age. It is a medium of explaining things easily so that anyone can understand. There are various types of comics you will find on the book store and online. Even you can read these tremendous African graphic novels. However, along with making comic stories and anime, there are other benefits of it. In technology, the comic is one of the top tools to teach and explain digital innovations and devices. In this article, you will find 7 fun comic illustrations that best describe machine learning.