Update README.md

README.md

@@ -4,19 +4,19 @@ language:
 license: mit
 tags:
 - automl
-- tabular
-- classification
-- sklearn
+- tabular-classification
+- autogluon
+- cmu-course
 datasets:
 - aedupuga/lego-sizes
 metrics:
-- accuracy
-- f1
+- type: accuracy
+- type: f1
 model-index:
 - name: Lego Brick Classification (Classical AutoML)
   results:
   - task:
-      type: classification
+      type: tabular-classification
       name: Tabular Classification
     dataset:
       name: aedupuga/lego-sizes
@@ -27,12 +27,24 @@ model-index:
       value: 0.97
     - type: f1
       value: 0.96
+  - task:
+      type: tabular-classification
+      name: Tabular Classification
+    dataset:
+      name: aedupuga/lego-sizes
+      type: classification
+      split: original
+    metrics:
+    - type: accuracy
+      value: 0.90
+    - type: f1
+      value: 0.89
 ---
 
 # Model Card for Lego Brick Classification (Classical AutoML)
 
-This model classifies LEGO pieces into three types — **Standard**, **Flat**, and **Sloped** — using their dimensions (Length, Height, Width, Studs).  
-It was trained with **AutoGluon Tabular AutoML**, selecting the best-performing algorithm (LightGBM).
+This model classifies LEGO pieces into three types — **Standard**, **Flat**, and **Sloped** — using their geometric dimensions (*Length, Height, Width, Studs*).  
+It was trained using **AutoGluon Tabular AutoML**, which automatically searched over classical ML models (LightGBM, XGBoost, CatBoost, Random Forest, k-NN, Neural Network) and selected the best-performing one.
 
 ---
 
@@ -56,7 +68,7 @@ It was trained with **AutoGluon Tabular AutoML**, selecting the best-performing
 
 ### Direct Use
 - Educational practice in **tabular classification**.  
-- Experimenting with AutoML search and hyperparameter tuning.
+- Experimenting with AutoML search and hyperparameter tuning.  
 
 ### Downstream Use
 - Could be used as a **teaching example** for AutoML pipelines on small tabular datasets.