Machine learning is among the most important branches of AI because it plays a key role in predicting the trend lines and behavior patterns of a large group of people using a dataset.
Artificial intelligence and machine learning are two of the hottest topics of discussion in recent times. We get to hear these terms very commonly due to their wide array of applications in different types of industries. Both these technologies are evolving at a great pace. Most importantly, a blend of the two makes it even more powerful. It is precisely the reason why the current trend lies in focusing on an appropriate combination of AI and ML to enhance its potential further. And this makes industries to gain more profits.
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.
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 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.
MLOps is the union of DevOps, machine learning, and data engineering. Built on DevOps’ existing approach, MLOps solutions are developed to increase re-usability, facilitate automation, manage data drift, model versioning, experiment tracking, continuous training and extract richer and consistent insights in a machine learning project.