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

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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 the performance of multiple models by showing 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 xyplots, such as ggplot2, lattice, and base. These packages offer different ways to create xyplots, but the basic idea is the same: to create an xyplot, you provide the data for the different models and the performance metrics you want to use.

xyplots 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 an xyplot showing the relation between the training time and accuracy scores. The model with the best performance across both metrics (shorter training time and higher accuracy) is the best model.

However, it’s important to note that xyplots 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, xyplots 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 xyplots in conjunction with other methods such as cross-validation to ensure that the selected model is robust and generalizes well to new data. The xyplot allows to see the relationship between different performance metrics and identify the trade-offs that may exist, making it easier to identify the best model that has a good performance across the metrics of interest.

 

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 xyplot.



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

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