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.
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
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?
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.
ML FAQ Part 1
What is ROC-AUC curve and how to interpret it?
Explain Type I and Type II errors.
What is Precision and Recall?
What is F1 score?
What is the difference between r-square and adjusted r-square values?
This post is part 2 in the series of frequently asked Machine Learning Interview Questions and Answers. Machine Learning Frequently asked Interview Questions and Answers Part 2
1. What is Feature Scaling and why and where it is needed?
2. Normalization vs Standardization
3. What is the bias-variance trade-off?
4. Define the Overfitting problem and why it occurs?
5. What are the methods to avoid Overfitting in ML?