Update README.md

README.md

@@ -135,12 +135,11 @@ 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"])
 
 ## AttentiveSkin
 To Predict Skin Corrosion/Irritation Potentials of Chemicals via Explainable Machine Learning Methods
@@ -149,7 +148,8 @@ Download: https://github.com/BeeBeeWong/AttentiveSkin/releases/tag/v1.0
 ## Tutorial
 
 ### Basic:
-AttentiveSkin is a software used for predicting GHS-defined (the Globally Harmonized System of Classification and Labeling of Chemicals) Skin Corrosion/Irritation labels of chemicals.
+AttentiveSkin is a software used for predicting GHS-defined 
+(the Globally Harmonized System of Classification and Labeling of Chemicals) Skin Corrosion/Irritation labels of chemicals.
 Download and unzip the "AttentiveSkin_v1.0.zip" at the URL above.
 Place the file "AttentiveSkin.exe" and dir "dependency" in the same directory.
 Launch the "AttentiveSkin.exe" and wait until the GUI being initialized.