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  In this blog, we will discuss about commonly used classification metrics. We will be covering  Accuracy Score ,  Confusion Matrix ,  Precision ,  Recall ,  F-Score ,  ROC-AUC  and will then learn how to extend them to the  multi-class classification . We will also discuss in which scenarios, which metric will be most suitable to use. First let’s understand some important terms used throughout the blog- True Positive (TP):  When you predict an observation belongs to a class and it actually does belong to that class. True Negative (TN):  When you predict an observation does not belong to a class and it actually does not belong to that class. False Positive (FP) : When you predict an observation belongs to a class and it actually does not belong to that class. False Negative(FN):  When you predict an observation does not belong to a class and it actually does belong to that class. All classification metrics work on these four terms. Let’s start understanding classification metrics- Accu

  Introduction In this article, we will see what are the list of commonly used commands in  Git . These are the commands which are really useful while working on any project. Hope this will be useful.  1) To initialize GitHub Repository in your local machine project folder: git init Git Copy 2) To get the status of files: git status Git Copy 3) To configure Username and Password: git config –global user.name “FirstName LastName” git config –global user.email “email@email.com” Git Copy 4) To clone the repository to your local machine: git clone ssh://john@example.com/path/to/my-project.git  Git Copy Note: Here URL is GitHub Repository URL 5) To add a single file to GitHub: git add file-name Git Copy 6) To add all the modified file to GitHub: git add . git add -A Git Copy 7) To commit the changes to GitHub: git commit -m “commit-message” Git Copy 8) Get the latest code from the main branch: git pull Git Copy 9) To pull the latest code from a particular branch: git pull branch-name Git Co