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 variables at once, it is a combination of a scatter plot and a line plot, where each variable is represented by a parallel line. 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 parallel plots, such as ggparallel, parallelplot and parallelcoord. These packages offer different ways to create parallel plots, but the basic idea is the same: to create a parallel plot, you provide the data for the different models and the performance metrics you want to use.

Parallel plots are useful for model selection because they allow you to quickly compare the performance of multiple models across different metrics, by visualizing the parallel lines of the metrics. For example, if you have trained several models using different algorithms and want to select the best one, you can create a parallel plot showing the distribution of their accuracy, precision and recall scores. The model with the best performance across all metrics is the best model.

However, it’s important to note that parallel plots 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, parallel plots are a useful tool for model selection in R, as they allow you to quickly compare the performance of multiple models across different metrics and identify which one has the best performance. It’s important to use parallel plots in conjunction with other methods such as cross-validation to ensure that the selected model is robust and generalizes well to new data. The combination of scatter plot and line plot allows to see the distribution of the performance metrics and the relative position of each model in relation to each metric, making it easier to identify the best model that has a good performance across all metrics.

 

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 parallel plot.



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

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