End-to-End Machine Learning: model selection in R using xyplot 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 an “xyplot”. An xyplot is a type of scatter plot that is used to compare …
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 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 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 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 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 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. …
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: 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 …