diff --git a/Snakefile b/Snakefile
index 1360dcb2..fde7af4b 100644
--- a/Snakefile
+++ b/Snakefile
@@ -230,7 +230,7 @@ if config["PARAMS_FOR_ANALYSIS"]["COMPUTE"]:
                                 rows_nan_threshold = config["PARAMS_FOR_ANALYSIS"]["ROWS_NAN_THRESHOLD"],
                                 cols_nan_threshold = config["PARAMS_FOR_ANALYSIS"]["COLS_NAN_THRESHOLD"]))
     files_to_compute.extend(expand(
-                                expand("data/processed/output_population_model/{min_valid_hours_per_day}hours_{min_valid_bins_per_hour}bins/{{rows_nan_threshold}}|{{cols_nan_threshold}}_{days_before_threshold}|{days_after_threshold}_{cols_var_threshold}/{source}_{day_segment}_{summarised}_{cv_method}_baseline.csv",
+                                expand("data/processed/output_population_model/{min_valid_hours_per_day}hours_{min_valid_bins_per_hour}bins/{{rows_nan_threshold}}|{{cols_nan_threshold}}_{days_before_threshold}|{days_after_threshold}_{cols_var_threshold}/baseline/{cv_method}/{source}_{day_segment}_{summarised}.csv",
                                     min_valid_hours_per_day=config["OVERALL_COMPLIANCE_HEATMAP"]["MIN_VALID_HOURS_PER_DAY"],
                                     min_valid_bins_per_hour=config["PHONE_VALID_SENSED_DAYS"]["MIN_VALID_BINS_PER_HOUR"],
                                     days_before_threshold = config["PARAMS_FOR_ANALYSIS"]["PARTICIPANT_DAYS_BEFORE_THRESHOLD"],
diff --git a/rules/models.snakefile b/rules/models.snakefile
index adc6e5d5..7187e4ba 100644
--- a/rules/models.snakefile
+++ b/rules/models.snakefile
@@ -157,9 +157,9 @@ rule baseline:
         rowsnan_colsnan_days_colsvar_threshold = "{rows_nan_threshold}|{cols_nan_threshold}_{days_before_threshold}|{days_after_threshold}_{cols_var_threshold}",
         demographic_features = config["PARAMS_FOR_ANALYSIS"]["DEMOGRAPHIC_FEATURES"]
     output:
-        "data/processed/output_population_model/{min_valid_hours_per_day}hours_{min_valid_bins_per_hour}bins/{rows_nan_threshold}|{cols_nan_threshold}_{days_before_threshold}|{days_after_threshold}_{cols_var_threshold}/{source}_{day_segment}_{summarised}_{cv_method}_baseline.csv"
+        "data/processed/output_population_model/{min_valid_hours_per_day}hours_{min_valid_bins_per_hour}bins/{rows_nan_threshold}|{cols_nan_threshold}_{days_before_threshold}|{days_after_threshold}_{cols_var_threshold}/baseline/{cv_method}/{source}_{day_segment}_{summarised}.csv"
     log:
-        "data/processed/output_population_model/{min_valid_hours_per_day}hours_{min_valid_bins_per_hour}bins/{rows_nan_threshold}|{cols_nan_threshold}_{days_before_threshold}|{days_after_threshold}_{cols_var_threshold}/{source}_{day_segment}_{summarised}_{cv_method}_notes.log"
+        "data/processed/output_population_model/{min_valid_hours_per_day}hours_{min_valid_bins_per_hour}bins/{rows_nan_threshold}|{cols_nan_threshold}_{days_before_threshold}|{days_after_threshold}_{cols_var_threshold}/baseline/{cv_method}/{source}_{day_segment}_{summarised}_notes.log"
     script:
         "../src/models/baseline.py"
  
@@ -192,7 +192,7 @@ rule merge_population_model_results:
     input:
         overall_results = "data/processed/output_population_model/{min_valid_hours_per_day}hours_{min_valid_bins_per_hour}bins/{rows_nan_threshold}|{cols_nan_threshold}_{days_before_threshold}|{days_after_threshold}_{cols_var_threshold}/{model}/{cv_method}/{source}_{day_segment}_{summarised}_{scaler}/overall_results.csv",
         nan_cells_ratio = "data/processed/data_for_population_model/{min_valid_hours_per_day}hours_{min_valid_bins_per_hour}bins/{rows_nan_threshold}|{cols_nan_threshold}_{days_before_threshold}|{days_after_threshold}_{cols_var_threshold}/{source}_{day_segment}_nancellsratio.csv",
-        baseline =  "data/processed/output_population_model/{min_valid_hours_per_day}hours_{min_valid_bins_per_hour}bins/{rows_nan_threshold}|{cols_nan_threshold}_{days_before_threshold}|{days_after_threshold}_{cols_var_threshold}/{source}_{day_segment}_{summarised}_{cv_method}_baseline.csv"
+        baseline =  "data/processed/output_population_model/{min_valid_hours_per_day}hours_{min_valid_bins_per_hour}bins/{rows_nan_threshold}|{cols_nan_threshold}_{days_before_threshold}|{days_after_threshold}_{cols_var_threshold}/baseline/{cv_method}/{source}_{day_segment}_{summarised}.csv"
     output:
         "data/processed/output_population_model/{min_valid_hours_per_day}hours_{min_valid_bins_per_hour}bins/{rows_nan_threshold}|{cols_nan_threshold}_{days_before_threshold}|{days_after_threshold}_{cols_var_threshold}/{model}/{cv_method}/{source}_{day_segment}_{summarised}_{scaler}/merged_population_model_results.csv"
     script:
diff --git a/src/models/modeling.py b/src/models/modeling.py
index 6271dee4..0639b744 100644
--- a/src/models/modeling.py
+++ b/src/models/modeling.py
@@ -104,7 +104,7 @@ for train_index, test_index in outer_cv.split(data_x):
     # Compute number of participants and features
     # values do not change between folds
     if fold_count == 1:
-        num_of_participants = train_x.shape[0] + test_x.shape[0]
+        num_of_rows = train_x.shape[0] + test_x.shape[0]
         num_of_features = train_x.shape[1]
 
     targets_value_counts = train_y["target"].value_counts()
@@ -150,7 +150,7 @@ else:
 # Step 4. Save results, parameters, and metrics to CSV files
 fold_predictions = pd.DataFrame({"fold_id": fold_id, "pid": pid, "hyperparameters": best_params, "true_y": true_y, "pred_y": pred_y, "pred_y_prob": pred_y_prob})
 fold_metrics = pd.DataFrame({"fold_id":[], "accuracy":[], "precision0": [], "recall0": [], "f10": [], "precision1": [], "recall1": [], "f11": [], "auc": [], "kappa": []})
-overall_results = pd.DataFrame({"num_of_participants": [num_of_participants], "num_of_features": [num_of_features], "rowsnan_colsnan_days_colsvar_threshold": [rowsnan_colsnan_days_colsvar_threshold], "model": [model], "cv_method": [cv_method], "source": [source], "scaler": [scaler], "day_segment": [day_segment], "summarised": [summarised], "accuracy": [metrics["accuracy"]], "precision0": [metrics["precision0"]], "recall0": [metrics["recall0"]], "f10": [metrics["f10"]], "precision1": [metrics["precision1"]], "recall1": [metrics["recall1"]], "f11": [metrics["f11"]], "auc": [metrics["auc"]], "kappa": [metrics["kappa"]]})
+overall_results = pd.DataFrame({"num_of_rows": [num_of_rows], "num_of_features": [num_of_features], "rowsnan_colsnan_days_colsvar_threshold": [rowsnan_colsnan_days_colsvar_threshold], "model": [model], "cv_method": [cv_method], "source": [source], "scaler": [scaler], "day_segment": [day_segment], "summarised": [summarised], "accuracy": [metrics["accuracy"]], "precision0": [metrics["precision0"]], "recall0": [metrics["recall0"]], "f10": [metrics["f10"]], "precision1": [metrics["precision1"]], "recall1": [metrics["recall1"]], "f11": [metrics["f11"]], "auc": [metrics["auc"]], "kappa": [metrics["kappa"]]})
 feature_importances_all_folds.insert(loc=0, column='fold_id', value=fold_id)
 feature_importances_all_folds.insert(loc=1, column='pid', value=pid)