While working in regression analysis, you should be familiar with some very basic but very impactful concepts. In machine learning interviews, you can always expects questions from regression analysis. Regression analysis also develop the basic understanding of machine learning model building as we mostly start our machine learning journey from regression analysis only.
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.
What is Covariance coefficient?
Covariance tells you whether two random variables vary with respect to each other or not. And if they vary together then whether they vary in same direction or in opposite direction with respect to each other. So if both random variables vary in same direction then we say it is positive covariance, however if they vary in opposite direction then it is negative covariance.
Let me start with simple question. Can we compare Mango and Apple? Both have different features in terms of tastes, sweetness, health benefits etc. So comparison can be performed between similar entities else it will be biased. Same logic applies to Machine Learning as well. Feature Scaling in Machine Learning brings features to the same scale before we apply any comparison or model building. Normalization and Standardization are the two frequently used techniques of Feature Scaling in Machine Learning.
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?