Slic Toolkit V3.2 May 2026

| Dataset | Time (v3.1) | Time (v3.2) | Accuracy | |---------------|-------------|-------------|----------| | BSD500 | 2.4 sec | 1.9 sec | 94.3% | | Medical MRI | 5.1 sec | 4.0 sec | 96.1% |

SLiC Toolkit v3.2 provides improved support for emerging technologies, including: slic toolkit v3.2

| Feature Category | Capabilities in v3.2 | |----------------|----------------------| | Imputation | 12 algorithms including MICE, MissForest, GAIN (Generative Adversarial Imputation Networks), and soft-impute with side information. | | Classification | 25+ classifiers with built-in missing-aware decision trees (e.g., surrogate splits, missing incorporated in attributes). | | Resampling | SMOTE variants (SMOTE-N, SMOTE-NC for mixed data), ADASYN, Tomek links, and NearMiss. | | Evaluation | Nested cross-validation, multiple scoring (AUC, Brier score, log-loss), and confidence intervals via bootstrap. | | Explainability | SHAP, LIME, partial dependence plots, and counterfactual explanations. | | Scalability | Out-of-core processing for datasets >1M rows; multi-GPU support (CUDA 12.x). | | Dataset | Time (v3