Fig. 6: Dimensionality reduction combined with Shapley additive explanations (SHAP) analysis.
From: Machine learning models to accelerate the design of polymeric long-acting injectables
Two-dimensional visualization of the SHAP values calculated for the input features of the LGBM model. The SHAP values for the 15 input features were condensed into two principal components using principal component analysis (PCA) and then grouped together using a simple unsupervised clustering algorithm (T-distributed Stochastic Neighbor Embedding). This low-dimensional/clustered plot was then utilized to visualize and compare the location of data instances corresponding to different input features, including T = 1.0; CL_Ratio; Polymer_MW; and Drug_Mw. In each case, the attached colorbar depicts the relative value of that feature in the dataset ranked from high (blue) to low (pink). Source data are provided as a Source Data file.
