Arpan·Jul 19Evaluating a Regression Model: How Do We Know Our Predictions Are Good?In the previous article, we built our first Simple Linear Regression model. The idea was simple: use YearsExperience to predict Salary. The…
Shruti Kaushik·Jul 5Why RMSE Isn’t Enough: Lessons from Evaluating Real-World Forecasting ModelsWhy RMSE Isn’t Enough: Lessons from Evaluating Real-World Forecasting Models
InPython in Plain EnglishbyRizki A. F.·Feb 7Evaluating Your Machine Learning ModelContinuing from where we left off (here), we’re now going to try to evaluate our model using data that’s been separated/split into test…
Vantakula Satya kiran·Jan 21, 2025Why MAE can’t be used as Loss Function in Machine Learning?In this blog we will learn about different loss functions, their advantages and disadvantages , threshold values, when to use what, and…
Faisal Bin Basha·Oct 16, 2024Evaulation Metrics For Regression AlgorithmsEvaluation metrics for machine learning models vary depending on whether the problem is a regression or classification task.
Laxman·Aug 24, 2024Evaluation metrics for Regression models:For regression models, the primary evaluation metrics are designed to measure the accuracy of predictions and the model’s ability to…
Sabbha Mondal·Aug 16, 2024Understanding MAE, MSE, and RMSE: Key Metrics in Machine LearningIn the world of machine learning, evaluating the performance of a model is crucial. This evaluation helps us understand how well our model…
InDevTechiebyDevTechie·Jul 27, 2024Linear Regression — Residual plot for Radio spend dataIn our last article, we said we will evaluate performance metrics and analyze Residual plot for Radio spend data. So let’s get going
Crystal X·Jul 5, 2024Introducing sklearn’s new function: root_mean_squared_errorRoot mean squared error, or RMSE, is one way to assess how a regression model fits a dataset. This is a regression model that tells the…
Jainvidip·Jun 27, 2024MSE vs RMSE vs MAEIn this blog we will learn about the difference between mean square error, root mean square error and mean absolute error
