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 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: automatic 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 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 scatterplot matrix 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 “scatterplot matrix” (SPLOM). A scatterplot matrix is a collection of scatter plots in …
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: 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 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. …
Evaluate Machine Learning Algorithm – repeated 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 “repeated k-fold cross validation.” In repeated k-fold cross validation, …
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 …