Tag Archives: supervised learning

End-to-End Machine Learning: manual grid search in R

End-to-End Machine Learning: manual grid search in R When training a machine learning model, it’s important to find the best set of parameters that will give the best performance on new, unseen data. One way to do this is by using a technique called “grid search.” Grid search is a method of systematically working through …

End-to-End Machine Learning: custom grid search in R

End-to-End Machine Learning: custom grid search in R When training a machine learning model, it’s important to find the best set of parameters that will give the best performance on new, unseen data. One way to do this is by using a technique called “grid search.” Grid search is a method of systematically working through …

End-to-End Machine Learning: statistical significance of difference between model predictions

  End-to-End Machine Learning: statistical significance of difference between model predictions In this Applied Machine Learning & Data Science Recipe (Jupyter Notebook), the reader will find the practical use of applied machine learning and data science in R programming: End-to-End Machine Learning: statistical significance of difference between model predictions.    End-to-End Machine Learning: statistical …

End-to-End Machine Learning: model selection in R using summary results

End-to-End Machine Learning: model selection in R using summary results When training multiple machine learning models, it’s important to select the best one to use on new, unseen data. One way to do this is by using a tabular method called “summary results”. Summary results are a collection of statistics that summarize the performance of …

End-to-End Machine Learning: model selection in R using parallel plot

End-to-End Machine Learning: model selection in R using parallel plot When training multiple machine learning models, it’s important to select the best one to use on new, unseen data. One way to do this is by using a visual tool called a “parallel plot”. A parallel plot is a visual representation that allows comparing multiple …

End-to-End Machine Learning: model selection in R using density plot

End-to-End Machine Learning: model selection in R using density plot When training multiple machine learning models, it’s important to select the best one to use on new, unseen data. One way to do this is by using a visual tool called a “density plot.” A density plot is a graphical representation of the probability density …

End-to-End Machine Learning: model selection in R using boxplot

End-to-End Machine Learning: model selection in R using boxplot When training multiple machine learning models, it’s important to select the best one to use on new, unseen data. One way to do this is by using a visual tool called a “boxplot.” A boxplot is a graphical representation of the distribution of a dataset, showing …

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: 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 …