End-to-End Machine Learning: model selection in R using scatterplot matrix

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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 a grid format, where each scatter plot shows the relationship between two variables. 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 scatterplot matrices, such as ggplot2, lattice, and pairs. These packages offer different ways to create SPLOMs, but the basic idea is the same: to create a SPLOM, you provide the data for the different models and the performance metrics you want to use.

Scatterplot matrices are useful for model selection because they allow you to quickly compare the performance of multiple models across different metrics, by visualizing the relationship between the metrics in the form of scatter plots. For example, if you have trained several models using different algorithms and want to select the best one, you can create a SPLOM 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 scatterplot matrices 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, scatterplot matrices 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 SPLOMs in conjunction with other methods such as cross-validation to ensure that the selected model is robust and generalizes well to new data. The scatterplot matrix allows to see the relationship between different performance metrics, 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 scatterplot matrix.



End-to-End Machine Learning: model selection in R using scatterplot matrix

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