Update README.md

README.md

@@ -172,7 +172,7 @@ then load, featurize, split, fit, and evaluate the catboost model
       representations = load_representations_from_dicts([{"name": "morgan"}, {"name": "maccs_rdkit"}]))
 
     model = load_model_from_dict({
-        "name": "cat_boost_classifier",
+        "name": "cat_boost_regressor",
         "config": {
             "x_features": ['SMILES::morgan', 'SMILES::maccs_rdkit'],
             "y_features": ['Solubility']}})
@@ -180,11 +180,11 @@ then load, featurize, split, fit, and evaluate the catboost model
     model.train(split_featurised_dataset["train"])
     preds = model.predict(split_featurised_dataset["test"])
     
-    classification_suite = load_suite("classification")
+    regression_suite = load_suite("regression")
     
-    scores = classification_suite.compute(
+    scores = regression_suite.compute(
         references=split_featurised_dataset["test"]['Solubility'],
-        predictions=preds["cat_boost_classifier::Solubility"])
+        predictions=preds["cat_boost_regressor::Solubility"])    
 
 
 ## Aqueous Solubility Data Curation