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diff --git a/catboost_info/learn/events.out.tfevents b/catboost_info/learn/events.out.tfevents
index 9e5374b..bdfcde0 100644
Binary files a/catboost_info/learn/events.out.tfevents and b/catboost_info/learn/events.out.tfevents differ
diff --git a/catboost_info/learn_error.tsv b/catboost_info/learn_error.tsv
index 4590590..5859d0f 100644
--- a/catboost_info/learn_error.tsv
+++ b/catboost_info/learn_error.tsv
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diff --git a/catboost_info/time_left.tsv b/catboost_info/time_left.tsv
index 520d0b5..7bd0002 100644
--- a/catboost_info/time_left.tsv
+++ b/catboost_info/time_left.tsv
@@ -1,1001 +1,1001 @@
 iter	Passed	Remaining
-0	2	2902
-1	5	2796
-2	8	2757
-3	11	2755
-4	13	2736
-5	17	2876
-6	22	3216
-7	26	3240
-8	29	3211
-9	31	3154
-10	34	3129
-11	37	3093
-12	40	3053
-13	43	3066
-14	48	3192
-15	52	3217
-16	55	3189
-17	57	3155
-18	60	3127
-19	63	3130
-20	66	3104
-21	69	3082
-22	73	3116
-23	77	3171
-24	80	3147
-25	83	3122
-26	86	3099
-27	89	3091
-28	92	3085
-29	94	3065
-30	97	3060
-31	103	3119
-32	106	3131
-33	109	3110
-34	112	3094
-35	114	3074
-36	118	3075
-37	121	3066
-38	123	3050
-39	126	3046
-40	132	3087
-41	135	3093
-42	138	3086
-43	141	3070
-44	144	3057
-45	146	3041
-46	149	3028
-47	151	3012
-48	155	3010
-49	159	3027
-50	162	3031
-51	165	3023
-52	168	3008
-53	171	2997
-54	173	2988
-55	176	2977
-56	179	2962
-57	182	2959
-58	186	2980
-59	190	2984
-60	193	2976
-61	196	2965
-62	198	2959
-63	201	2949
-64	204	2942
-65	207	2931
-66	210	2927
-67	214	2946
-68	218	2949
-69	221	2942
-70	224	2933
-71	226	2925
-72	229	2918
-73	232	2911
-74	235	2902
-75	238	2901
-76	243	2914
-77	246	2915
-78	249	2911
-79	252	2902
-80	255	2893
-81	257	2887
-82	260	2879
-83	263	2871
-84	266	2869
-85	270	2878
-86	274	2884
-87	277	2879
-88	280	2871
-89	283	2863
-90	286	2857
-91	288	2848
-92	291	2841
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diff --git a/catboost_info/tmp/cat_feature_index.704a9020-9e036d5d-7e7f044c-be502316.tmp b/catboost_info/tmp/cat_feature_index.704a9020-9e036d5d-7e7f044c-be502316.tmp
new file mode 100644
index 0000000..593f470
Binary files /dev/null and b/catboost_info/tmp/cat_feature_index.704a9020-9e036d5d-7e7f044c-be502316.tmp differ
diff --git a/models/CatBoostRegressor.model b/models/CatBoostRegressor.model
index ed191a8..f1e09f8 100644
Binary files a/models/CatBoostRegressor.model and b/models/CatBoostRegressor.model differ
diff --git a/train_model.py b/train_model.py
index 92d3c18..7c8b51b 100644
--- a/train_model.py
+++ b/train_model.py
@@ -54,12 +54,9 @@ from sklearn.utils.validation import check_X_y
 import joblib
 
 from catboost import CatBoostRegressor
-from lightgbm import LGBMRegressor
-from xgboost import XGBRegressor
 from sklearn.ensemble import RandomForestRegressor
-from deepforest import CascadeForestRegressor
 
-Regressor = Union[CatBoostRegressor, LGBMRegressor, XGBRegressor]
+Regressor = Union[CatBoostRegressor, RandomForestRegressor]
 
 
 def train_model(regType: Regressor):
@@ -79,7 +76,7 @@ def train_model(regType: Regressor):
 
 for reg in [
         #CatBoostRegressor, LGBMRegressor, XGBRegressor, RandomForestRegressor,
-        CascadeForestRegressor
+        CatBoostRegressor
 ]:
     train_model(reg)
 
@@ -90,23 +87,28 @@ from sklearn import metrics
 def eval_model(regType: Regressor):
     models = joblib.load(f"models/{regType.__name__}.model")
     X, ys = train_test_data['X_test'], train_test_data['y_test']
-    if isinstance(X, CascadeForestRegressor):
-        X = X.values
     evals = []
     for target_col, reg in models.items():
         y_hat = reg.predict(X)  # fake
         y = ys[target_col]  # real
+        dy = (y - y_hat).abs()
         rmse = metrics.mean_squared_error(y, y_hat, squared=False)
         r2 = metrics.r2_score(y, y_hat)
-        eval_dict = {'Error': target_col, 'RMSE': rmse, 'R^2': r2}
+        eval_dict = {
+            'Error': target_col,
+            'RMSE': rmse,
+            'R^2': r2,
+            "err_max": dy.max(),
+            "err_min": dy.min(),
+            "err_mean": dy.mean(),
+        }
         evals.append(eval_dict)
     print(regType.__name__)
     print(pd.DataFrame(evals))
 
 
 for reg in [
-        CatBoostRegressor, LGBMRegressor, XGBRegressor, RandomForestRegressor,
-        CascadeForestRegressor
+        CatBoostRegressor,
 ]:
     eval_model(reg)
 # %%
diff --git a/train_model_copy.py b/train_model_copy.py
index 8951fa0..68efeab 100644
--- a/train_model_copy.py
+++ b/train_model_copy.py
@@ -20,11 +20,7 @@ from sklearn.model_selection import train_test_split
 
 feature_cols = ['elapsed_seconds'
                 ] + get_state_vect_cols('pred') + get_state_vect_cols('start')
-# The target values are the errors between the physical model predictions
-# and the ground truth observations
 target_cols = get_state_vect_cols('err')
-print(target_cols)
-# Create feature and target matrices
 X = df[feature_cols]
 y = df[target_cols]
 data_keys = ['X_train', 'X_test', 'y_train', 'y_test']
@@ -64,12 +60,17 @@ def eval_model():
     for target_col, reg in models.items():
         y_hat = reg.predict(X)  # fake
         y = ys[target_col]  # real
-        print(y_hat[:10])
-        print(y[:10])
-        print("-----")
+        dy = (y - y_hat).abs()
         rmse = metrics.mean_squared_error(y, y_hat, squared=False)
         r2 = metrics.r2_score(y, y_hat)
-        eval_dict = {'Error': target_col, 'RMSE': rmse, 'R^2': r2}
+        eval_dict = {
+            'Error': target_col,
+            'RMSE': rmse,
+            'R^2': r2,
+            "err_max": dy.max(),
+            "err_min": dy.min(),
+            "err_mean": dy.mean(),
+        }
         evals.append(eval_dict)
     print(pd.DataFrame(evals))