include ccp_alpha hyper-parameter in advance features (#1)

- include ccp_alpha hyper-parameter in advance features (6573d9d346b0612fb6321b19d700f1ead92de09d)


Co-authored-by: Gourav Sharma <[email protected]>

app.py

@@ -105,6 +105,14 @@ if toggle:
                                                    min_value=0.0,
                                                    help="""A node will be split if this split induces a decrease of the 
                                                    impurity greater than or equal to this value.""")
+    ccp_alpha = st.sidebar.number_input("ccp_alpha",
+                            min_value=0,
+                            max_value=30,
+                            step=0.1,
+                            value=0,
+                            help="""Complexity parameter used for Minimal Cost-Complexity Pruning. 
+                            The subtree with the largest cost complexity that is smaller than ccp_alpha will be chosen. 
+                            By default, no pruning is performed.""")
 train = st.sidebar.button("Train Model", type="primary")
 if st.sidebar.button("Reset"):
     st.experimental_rerun()
@@ -120,7 +128,8 @@ if train:
                                 max_features=max_features,
                                 random_state=random_state,
                                 max_leaf_nodes=max_leaf_nodes,
-                                min_impurity_decrease=min_impurity_decrease)
+                                min_impurity_decrease=min_impurity_decrease,
+                                ccp_alpha = ccp_alpha)
     clf.fit(x_train[:, :2], y_train)
     x_pred = clf.predict(x_train[:,:2])
     y_pred = clf.predict(x_test[:, :2])