End-to-End Machine Learning: rsquared metric in R When training a machine learning model, it’s important to evaluate its performance to understand how well it will work on new, unseen data. One common way to evaluate the performance of a model for regression problems is by using a metric called “R-squared” (R²) R-squared is a measure …
Day: November 23, 2019
End-to-End Machine Learning: roc metric in R When training a machine learning model, it’s important to evaluate its performance to understand how well it will work on new, unseen data. One common way to evaluate the performance of a model for binary classification problems is by using a metric called “Receiver Operating Characteristic” (ROC) curve. …
End-to-End Machine Learning: rmse metric in R When training a machine learning model, it’s important to evaluate its performance to understand how well it will work on new, unseen data. One common way to evaluate the performance of a model is by using a metric called “root mean squared error” (RMSE). RMSE is a measure …
End-to-End Machine Learning: logloss metric in R When training a machine learning model, it’s important to evaluate its performance to understand how well it will work on new, unseen data. One common way to evaluate the performance of a model is by using a metric called “log loss” or “cross-entropy loss”. Log loss is a …
End-to-End Machine Learning: kappa metric in R When training a machine learning model, it’s important to evaluate its performance to understand how well it will work on new, unseen data. One common way to evaluate the performance of a model is by using a metric called “kappa.” Kappa is a measure of the agreement between …