Metrics
Learn which metrics to use for a machine learning use case and model. MAPE,RMSE,MAE,MDAPE,precision, recall, and many more metrics explained simply
Metrics for imbalanced data (simply explained) Paid Members Public
Imbalanced data is a common occurrence when working with classification machine learning models. In this post, I explain which metrics are best to use when its present in your dataset.
What is a good RMSE value? Simply explained Paid Members Public
Root Mean Squared Error (RMSE) is a popular metric to use for regression machine learning models, but what is good value? In this post, I explain what RMSE is, what a good value is, and answer some common questions.
RMSE vs MAE, which is the best regression metric? Paid Members Public
RMSE and MAE are both metrics for measuring the performance of regression machine learning models, but what’s the difference? In this post, I will explain what these metrics are, their differences, and help you decide which is best for your project.
RMSE vs MSE, what's the difference? Paid Members Public
RMSE and MSE are both metrics for measuring the performance of regression machine learning models, but what’s the difference? In this post, I will explain what these metrics are, their differences, and help you decide which is best for your project.
Loss function vs cost function, what’s the difference? Paid Members Public
Loss function and cost function are two terms that are used in similar contexts within machine learning, which can lead to confusion as to what the difference is. In this post I will explain what they are, their similarities, and their differences.
Micro vs Macro F1 score, what’s the difference? Paid Members Public
Micro average and macro average are aggregation methods for F1 score, a metric which is used to measure the performance of classification machine learning models. They are often shown together, which can make it confusing to know what the difference is.
F1 score vs accuracy, which is the best classification metric? Paid Members Public
F1 score and accuracy are machine learning metrics for classification models, but which should you use for your project? In this post I will explain what they are, their differences, and help you decide which is the best choice for you.
F1 score vs AUC, which is the best classification metric? Paid Members Public
F1 score and AUC are machine learning metrics for classification models, but which should you use for your project? In this post I will explain what they are, their differences, and help you decide which is the better choice for you.
