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Random Forest Similarity Maps: A Scalable Visual Representation for Global and Local Interpretation
Machine Learning prediction algorithms have made significant contributions in today's world, leading to increased usage in various domains. However, as ML algorithms surge, the need for transparent and interpretable models becomes essential. Visual representations have shown to be instrumental in addressing such an issue, allowing users to grasp models' inner workings. Despite their popularity, visualization techniques still present visual scalability limitations, mainly when applied to analyzedoi:10.3390/electronics10222862 fatcat:ymug6llu4fbttd3nuqfmzcuqp4