partial dependance for numerical multiclass tasks

autoML.py

@@ -169,11 +169,13 @@ def autoML(csv, task, budget, label, metric_to_minimize_class, metric_to_minimiz
     with tab3:
         with st.container():
             st.subheader('1D Partial Dependance for the three most important features')
+            st.write(len(set(y)))
+            st.write(set(y))
             
             l_col_1D = list(st.columns((1,1,1)))
 
             common_params = {
-                        "subsample": 25,
+                        "subsample": 50,
                         "n_jobs": 2,
                         "grid_resolution": 20,
                         "random_state": 0
@@ -194,6 +196,7 @@ def autoML(csv, task, budget, label, metric_to_minimize_class, metric_to_minimiz
                         pipeline,
                         df_features,
                         **features_info,
+                        target=len(set(y)),
                         ax=ax,
                         **common_params,
                     )
@@ -225,6 +228,7 @@ def autoML(csv, task, budget, label, metric_to_minimize_class, metric_to_minimiz
                             pipeline,
                             df_features,
                             **features_info,
+                            target=len(set(y)),
                             ax=ax,
                             **common_params,
                         )