How can you use R to create visualizations for machine learning models?

3 Clustering plots

Clustering is a type of unsupervised learning that groups similar observations based on their input variables. To visualize clustering models, you can use the cluster package, which provides functions for clustering analysis and visualization in R. For example, you can use the clusplot function to create a plot that shows the clusters and their centroids in a two-dimensional space. You can also use the silhouette function to calculate and plot the silhouette coefficients, which measure how well each observation fits into its assigned cluster. Additionally, you can use the factoextra package, which provides functions to visualize the results of various clustering methods, such as k-means, hierarchical, or density-based clustering.

4 Dimensionality reduction plots

Dimensionality reduction is a technique that reduces the number of input variables while preserving the essential information and structure of the data. To visualize dimensionality reduction models, you can use the ggbiplot package, which extends the ggplot2 package to create biplots for principal component analysis (PCA) and other methods. For example, you can use the ggbiplot function to create a plot that shows the observations and the variables in a reduced space, with options to color, size, or shape the points by a grouping variable. You can also use the ggscreeplot function to create a scree plot that shows the proportion of variance explained by each principal component. Additionally, you can use the factoextra package, which provides functions to visualize the results of various dimensionality reduction methods, such as PCA, multidimensional scaling (MDS), or t-distributed stochastic neighbor embedding (t-SNE).

5 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?