add R2

stream_app.py

@@ -197,8 +197,6 @@ fig = px.bar(xgb_features_importance,
 st.plotly_chart(fig)
 
 st.subheader("Plot predicted vs real")
-import plotly.graph_objs as go
-
 compare = pd.concat([pd.DataFrame({'target': target_test, 'predicted': target_test_predicted, 'sample': 'test'}),
                      pd.DataFrame({'target': target_train, 'predicted': target_train_predicted, 'sample': 'train'})])
 fig = px.scatter(
@@ -214,6 +212,31 @@ fig = px.scatter(
 
 st.plotly_chart(fig)
 
+
+naive_error_std = np.std(target_train - np.mean(target_train_predicted))
+model_error_std = np.std(target_train - target_train_predicted)
+
+st.metric(label="Naive error standard deviation", value=naive_error_std)
+st.metric(label="Model error standard deviation", value=model_error_std)
+
+corr_matrix = np.corrcoef(target_train, target_train_predicted)
+R_sq = corr_matrix[0, 1] ** 2
+st.metric(label="Explained variation thanks to model (R^2)",  value=f"{round(100*R_sq, 2)}%")
+
+naive_error_std = np.std(target_test - np.mean(target_test_predicted))
+model_error_std = np.std(target_test - target_test_predicted)
+
+st.metric(label="Naive error standard deviation", value=naive_error_std)
+st.metric(label="Model error standard deviation", value=model_error_std)
+
+corr_matrix = np.corrcoef(target_test, target_test_predicted)
+R_sq = corr_matrix[0, 1] ** 2
+st.metric(label="Explained variation thanks to model (R^2)",  value=f"{round(100*R_sq, 2)}%")
+
+
 sample_revenues = st.sidebar.number_input('Yearly revenues', value=1000000)
 authority = st.sidebar.selectbox('Organization country', predictors.org_country.cat.categories)
 authority = st.sidebar.selectbox('Organization activity', predictors.org_company_type.cat.categories)
+
+
+