Tag Archives: data science

End-to-End Machine Learning: rsquared metric in R

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 …

End-to-End Machine Learning: roc metric in R

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

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

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

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 …

End-to-End Machine Learning: accuracy metric in R

End-to-End Machine Learning: accuracy 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 “accuracy.” Accuracy is a measure of how often the …

Evaluate Machine Learning Algorithm – leave one out cross validation in R

Evaluate Machine Learning Algorithm – leave one out cross validation in R Evaluating the performance of a machine learning algorithm is an important step in understanding how well it will work on new, unseen data. One popular method for evaluating the performance of an algorithm is called “leave-one-out cross validation” (LOOCV). In leave-one-out cross validation, …

Evaluate Machine Learning Algorithm in R – kfold cross validation in R

Evaluate Machine Learning Algorithm in R – kfold cross validation in R Evaluating the performance of a machine learning algorithm is an important step in understanding how well it will work on new, unseen data. One popular method for evaluating the performance of an algorithm is called “k-fold cross validation.” In k-fold cross validation, the …

Evaluate Machine Learning Algorithm in R – dataset split in R

Evaluate Machine Learning Algorithm in R – dataset split in R Evaluating the performance of a machine learning algorithm is an important step in understanding how well it will work on new, unseen data. One common method for evaluating the performance of an algorithm is to split the available data into two sets: a training …

Evaluate Machine Learning Algorithm in R – bootstrap in R

Evaluate Machine Learning Algorithm in R – bootstrap in R Evaluating the performance of a machine learning algorithm is an important step in understanding how well it will work on new, unseen data. One popular method for evaluating the performance of an algorithm is called “bootstrapping.” Bootstrapping is a resampling method that creates multiple new …