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

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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 function of a dataset, it shows the probability of a particular value appearing in the dataset. 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 density plots, such as ggplot2, lattice, and base. These packages offer different ways to create density plots, but the basic idea is the same: to create a density plot, you provide the data for the different models and the performance metric you want to use.

Density plots are useful for model selection because they allow you to quickly compare the distribution of performance metrics of multiple models, by visualizing the probability density function of the metric. For example, if you have trained several models using different algorithms and want to select the best one, you can create a density plot showing the distribution of their accuracy scores. The model with the highest peak in the density plot is the best model.

However, it’s important to note that density 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, density plots are a useful tool for model selection in R, as they allow you to quickly compare the distribution of performance metrics of multiple models and identify which one has the best performance. It’s important to use density plots 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 density plot.



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

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