Yash Chaudhariyash-chaudharis-blog.hashnode.dev·Nov 11, 2024Error Metrics in Regression and ClassificationHi there, in this post, I will discuss the metrics that are used to evaluate how accurate machine learning models are. I hope that helps. You can use this page as a cheat sheet. At its core, what we all do when deriving a machine learning model is ma...Discuss#MSE
Steve Worthamstevewortham.hashnode.dev·Nov 5, 2024Visual Regression Testing Using PlaywrightAmid the fast-moving process of delivering software, the time spent on reviewing web pages manually is progressively reducing. In order to maintain the speed of modern development, we require tools that help keep our designs and layouts preserved and...Discuss#Regression
Anix LynchProanixblog.hashnode.dev·Nov 5, 2024Part 1: 11 Basic Machine Learning Techniques with Math Notation Friendly Explained1. Linear Regression Linear Regression is one of the simplest techniques for predicting a continuous outcome by modeling the relationship between an independent variable and a dependent variable with a straight line. Key Concept:The goal of linear re...Discuss#Regression
Steve Worthamstevewortham.hashnode.dev·Oct 30, 2024Test Cases Prioritization For Regression Testing [Methods & Best Practices]One of the most critical software development and maintenance aspects is regression testing. To avoid unwanted changes, a set of test cases is run whenever software is modified, and the new outputs are compared to the older ones. If the new and old o...DiscussTesting
Deepak Kumar Mohantykumarblog-1.hashnode.dev·Oct 26, 2024Understanding Error Metrics in Regression: MSE, MAE, and RMSEWhen building predictive models, especially in regression tasks, evaluating their performance is crucial. This is where error metrics come into play. In this blog post, we will explore three common error metrics: Mean Squared Error (MSE), Mean Absolu...Discussroot mean square error
Fatima Jannetmahia.hashnode.dev·Oct 26, 2024Machine Learning Regression Model Selection in PythonHello, welcome back to ML! So far, we have covered regressions, and this blog is about choosing the right regression model. Which one should you apply to your model? Which one you should choose? You'll find all your answers in this blog. I can confid...Discuss·37 readsMachine Learning (Python)Python
Fatima Jannetmahia.hashnode.dev·Oct 25, 2024Machine Learning Chapter 2.6: Random Forest RegressionHello and welcome back to Machine Learning! Today, we'll learn about the intuition behind random forests and how to apply them step by step in Python. Let's get started. This will be our final blog on regression. Intuition Random forest is a version ...Discuss·55 readsMachine Learning (Python)Python
Fatima Jannetmahia.hashnode.dev·Oct 22, 2024Machine Learning Chapter 2.3: Polynomial RegressionWelcome back to Machine Learning! Today we’ll talk about Polynomial Regression Polynomial Regression Intuition If you look carefully, polynomial regression is very similar to the multiple linear regression. But, instead of different variable, x1 is d...Discuss·37 readsMachine Learning (Python)Data Science
Fatima Jannetmahia.hashnode.dev·Oct 21, 2024Machine Learning Chapter 2.2: Multiple Linear RegressionWelcome to Part 2.2 of Machine Learning! Here is the equation for multiple linear regression. As you can see, it is quite similar to our linear regression model. Assumptions of linear regression Now, let's look at the first dataset for the linear re...DiscussMachine Learning (Python)Machine Learning
Al Shahriar Abidshahriarabid.hashnode.dev·Oct 18, 2024Simple Linear Regression: A Beginners Guide.Hi there! If you’re new to machine learning, like me, one of the first topics you will encounter is Simple Linear Regression. It is used to predict a variable based on another variable. $$\hat{y} = b _ { 0 } + b _ { 1 } X _ { 1 }$$Here: y hat = Depe...DiscussGeneral Programming
![Test Cases Prioritization For Regression Testing [Methods & Best Practices]](https://cdn.hashnode.com/res/hashnode/image/upload/v1730284547562/790ea2c8-fabc-4afa-8bae-9c8b1e60f250.jpeg?w=1600&h=840&fit=crop&crop=entropy&auto=compress,format&format=webp)
