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

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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 the minimum, first quartile, median, third quartile, and maximum values. It’s a useful tool for comparing the performance of multiple models by showing the distribution of their performance metrics.

In R, there are several packages that provide functions to create boxplots, such as ggplot2, lattice, and base. These packages offer different ways to create boxplots, but the basic idea is the same: to create a boxplot, you provide the data for the different models and the performance metric you want to use.

Boxplots are useful for model selection because they allow you to quickly compare the performance of multiple models and identify which one has the best performance. For example, if you have trained several models using different algorithms and want to select the best one, you can create a boxplot showing the distribution of their accuracy scores. The model with the highest median and smallest spread, and less outliers is the best model.

However, it’s important to note that boxplots should be used in conjunction with other methods such as cross-validation, to ensure that the model selected is robust and generalizes well to new data.

Overall, boxplots are a useful tool for model selection in R, as they allow you to quickly compare the performance of multiple models and identify which one has the best performance. It’s important to use boxplots in conjunction with other methods such as cross-validation to ensure that the selected model is robust and generalizes well to new data.

 

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: model selection in R using boxplot.



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

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