diff --git a/exploration/ml_pipeline_daily.py b/exploration/ml_pipeline_daily.py
deleted file mode 100644
index e12cc1f..0000000
--- a/exploration/ml_pipeline_daily.py
+++ /dev/null
@@ -1,284 +0,0 @@
-# ---
-# jupyter:
-#   jupytext:
-#     formats: ipynb,py:percent
-#     text_representation:
-#       extension: .py
-#       format_name: percent
-#       format_version: '1.3'
-#       jupytext_version: 1.13.0
-#   kernelspec:
-#     display_name: straw2analysis
-#     language: python
-#     name: straw2analysis
-# ---
-
-# %% jupyter={"source_hidden": true}
-# %matplotlib inline
-import datetime
-import importlib
-import os
-import sys
-
-import numpy as np
-import matplotlib.pyplot as plt
-import pandas as pd
-import seaborn as sns
-import yaml
-from pyprojroot import here
-from sklearn import linear_model, svm, kernel_ridge, gaussian_process
-from sklearn.model_selection import LeaveOneGroupOut, cross_val_score
-from sklearn.metrics import mean_squared_error, r2_score
-from sklearn.impute import SimpleImputer
-
-nb_dir = os.path.split(os.getcwd())[0]
-if nb_dir not in sys.path:
-    sys.path.append(nb_dir)
-
-import machine_learning.features_sensor
-import machine_learning.labels
-import machine_learning.model
-
-# %% [markdown]
-# # RAPIDS models
-
-# %% [markdown]
-# ## PANAS negative affect
-
-# %% jupyter={"source_hidden": true}
-# model_input = pd.read_csv("../data/input_PANAS_NA.csv") # Nestandardizirani podatki - pred temeljitim čiščenjem
-model_input = pd.read_csv("../data/z_input_PANAS_NA.csv") # Standardizirani podatki - pred temeljitim čiščenjem
-# %% [markdown]
-# ### NaNs before dropping cols and rows
-
-# %% jupyter={"source_hidden": true}
-sns.set(rc={"figure.figsize":(16, 8)})
-sns.heatmap(model_input.sort_values('pid').set_index('pid').isna(), cbar=False)
-
-# %% jupyter={"source_hidden": true}
-nan_cols = list(model_input.loc[:, model_input.isna().all()].columns)
-nan_cols
-
-# %% jupyter={"source_hidden": true}
-model_input.dropna(axis=1, how="all", inplace=True)
-model_input.dropna(axis=0, how="any", subset=["target"], inplace=True)
-
-# %% [markdown]
-# ### NaNs after dropping NaN cols and rows where target is NaN
-
-# %% jupyter={"source_hidden": true}
-sns.set(rc={"figure.figsize":(16, 8)})
-sns.heatmap(model_input.sort_values('pid').set_index('pid').isna(), cbar=False)
-
-# %% jupyter={"source_hidden": true}
-index_columns = ["local_segment", "local_segment_label", "local_segment_start_datetime", "local_segment_end_datetime"]
-
-model_input.set_index(index_columns, inplace=True)
-
-cv_method = '5kfold'
-if cv_method == 'half_logo':
-    data_x, data_y, data_groups = model_input.drop(["target", "pid"], axis=1), model_input["target"], model_input["pid"]
-else:
-    model_input['pid_index'] = model_input.groupby('pid').cumcount()
-    model_input['pid_count'] = model_input.groupby('pid')['pid'].transform('count')
-
-    model_input["pid_index"] = (model_input['pid_index'] / model_input['pid_count'] + 1).round()
-    model_input["pid_half"] = model_input["pid"] + "_" +  model_input["pid_index"].astype(int).astype(str)
-
-    data_x, data_y, data_groups = model_input.drop(["target", "pid", "pid_index", "pid_half"], axis=1), model_input["target"], model_input["pid_half"]
-
-# %% jupyter={"source_hidden": true}
-categorical_feature_colnames = ["gender", "startlanguage"]
-
-# %% jupyter={"source_hidden": true}
-categorical_features = data_x[categorical_feature_colnames].copy()
-
-# %% jupyter={"source_hidden": true}
-mode_categorical_features = categorical_features.mode().iloc[0]
-
-# %% jupyter={"source_hidden": true}
-# fillna with mode
-categorical_features = categorical_features.fillna(mode_categorical_features)
-
-# %% jupyter={"source_hidden": true}
-# one-hot encoding
-categorical_features = categorical_features.apply(lambda col: col.astype("category"))
-if not categorical_features.empty:
-    categorical_features = pd.get_dummies(categorical_features)
-
-# %% jupyter={"source_hidden": true}
-numerical_features = data_x.drop(categorical_feature_colnames, axis=1)
-
-# %% jupyter={"source_hidden": true}
-train_x = pd.concat([numerical_features, categorical_features], axis=1)
-
-# %% jupyter={"source_hidden": true}
-train_x.dtypes
-
-# %% jupyter={"source_hidden": true}
-logo = LeaveOneGroupOut()
-logo.get_n_splits(
-    train_x,
-    data_y,
-    groups=data_groups,
-)
-
-# Defaults to 5 k folds in cross_validate method
-if cv_method != 'logo' and cv_method != 'half_logo':
-    logo = None
-
-# %% jupyter={"source_hidden": true}
-sum(data_y.isna())
-
-# %% [markdown]
-# ### Linear Regression
-
-# %% jupyter={"source_hidden": true}
-lin_reg_rapids = linear_model.LinearRegression()
-
-# %% jupyter={"source_hidden": true}
-imputer = SimpleImputer(missing_values=np.nan, strategy='mean')
-
-# %% jupyter={"source_hidden": true}
-lin_reg_scores = cross_val_score(
-    lin_reg_rapids,
-    X=imputer.fit_transform(train_x),
-    y=data_y,
-    groups=data_groups,
-    cv=logo,
-    n_jobs=-1,
-    scoring='r2'
-)
-lin_reg_scores
-np.median(lin_reg_scores)
-
-# %% [markdown]
-# ### Ridge regression
-
-# %% jupyter={"source_hidden": true}
-ridge_reg = linear_model.Ridge(alpha=.5)
-
-# %% tags=[] jupyter={"source_hidden": true}
-ridge_reg_scores = cross_val_score(
-    ridge_reg,
-    X=imputer.fit_transform(train_x),
-    y=data_y,
-    groups=data_groups,
-    cv=logo,
-    n_jobs=-1,
-    scoring="r2"
-)
-np.median(ridge_reg_scores)
-
-# %% [markdown]
-# ### Lasso
-
-# %% jupyter={"source_hidden": true}
-lasso_reg = linear_model.Lasso(alpha=0.1)
-
-# %% jupyter={"source_hidden": true}
-lasso_reg_score = cross_val_score(
-    lasso_reg,
-    X=imputer.fit_transform(train_x),
-    y=data_y,
-    groups=data_groups,
-    cv=logo,
-    n_jobs=-1,
-    scoring="r2"
-)
-np.median(lasso_reg_score)
-
-# %% [markdown]
-# ### Bayesian Ridge
-
-# %% jupyter={"source_hidden": true}
-bayesian_ridge_reg = linear_model.BayesianRidge()
-
-# %% jupyter={"source_hidden": true}
-bayesian_ridge_reg_score = cross_val_score(
-    bayesian_ridge_reg,
-    X=imputer.fit_transform(train_x),
-    y=data_y,
-    groups=data_groups,
-    cv=logo,
-    n_jobs=-1,
-    scoring="r2"
-)
-np.median(bayesian_ridge_reg_score)
-
-# %% [markdown]
-# ### RANSAC (outlier robust regression)
-
-# %% jupyter={"source_hidden": true}
-ransac_reg = linear_model.RANSACRegressor()
-
-# %% jupyter={"source_hidden": true}
-np.median(
-    cross_val_score(
-    ransac_reg,
-    X=imputer.fit_transform(train_x),
-    y=data_y,
-    groups=data_groups,
-    cv=logo,
-    n_jobs=-1,
-    scoring="r2"
-    )
-)
-
-# %% [markdown]
-# ### Support vector regression
-
-# %% jupyter={"source_hidden": true}
-svr = svm.SVR()
-
-# %% jupyter={"source_hidden": true}
-np.median(
-    cross_val_score(
-    svr,
-    X=imputer.fit_transform(train_x),
-    y=data_y,
-    groups=data_groups,
-    cv=logo,
-    n_jobs=-1,
-    scoring="r2"
-    )
-)
-
-# %% [markdown]
-# ### Kernel Ridge regression
-
-# %% jupyter={"source_hidden": true}
-kridge = kernel_ridge.KernelRidge()
-
-# %% jupyter={"source_hidden": true}
-np.median(
-    cross_val_score(
-        kridge,
-        X=imputer.fit_transform(train_x),
-        y=data_y,
-        groups=data_groups,
-        cv=logo,
-        n_jobs=-1,
-        scoring="r2"
-    )
-)
-# %% [markdown]
-# ### Gaussian Process Regression
-
-# %% jupyter={"source_hidden": true}
-gpr = gaussian_process.GaussianProcessRegressor()
-
-# %% jupyter={"source_hidden": true}
-
-np.median(
-    cross_val_score(
-        gpr,
-        X=imputer.fit_transform(train_x),
-        y=data_y,
-        groups=data_groups,
-        cv=logo,
-        n_jobs=-1,
-        scoring="r2"
-    )
-)
-# %%
diff --git a/exploration/ml_pipeline_daily_cleaned_daily.py b/exploration/ml_pipeline_daily_cleaned_daily.py
deleted file mode 100644
index 37b973a..0000000
--- a/exploration/ml_pipeline_daily_cleaned_daily.py
+++ /dev/null
@@ -1,332 +0,0 @@
-# ---
-# jupyter:
-#   jupytext:
-#     formats: ipynb,py:percent
-#     text_representation:
-#       extension: .py
-#       format_name: percent
-#       format_version: '1.3'
-#       jupytext_version: 1.13.0
-#   kernelspec:
-#     display_name: straw2analysis
-#     language: python
-#     name: straw2analysis
-# ---
-
-# %% jupyter={"source_hidden": true}
-# %matplotlib inline
-import datetime
-import importlib
-import os
-import sys
-
-import numpy as np
-import matplotlib.pyplot as plt
-import pandas as pd
-import seaborn as sns
-import yaml
-from pyprojroot import here
-from sklearn import linear_model, svm, kernel_ridge, gaussian_process
-from sklearn.model_selection import LeaveOneGroupOut, cross_val_score, cross_validate
-from sklearn.metrics import mean_squared_error, r2_score
-from sklearn.impute import SimpleImputer
-from sklearn.dummy import DummyRegressor
-import xgboost as xg
-from IPython.core.interactiveshell import InteractiveShell
-InteractiveShell.ast_node_interactivity = "all"
-
-nb_dir = os.path.split(os.getcwd())[0]
-if nb_dir not in sys.path:
-    sys.path.append(nb_dir)
-
-import machine_learning.features_sensor
-import machine_learning.labels
-import machine_learning.model
-
-# %% [markdown]
-# # RAPIDS models
-
-# %% [markdown]
-# ## PANAS negative affect
-
-# %% jupyter={"source_hidden": true}
-model_input = pd.read_csv("../data/daily_18_hours_all_targets/input_PANAS_negative_affect_mean.csv")
-
-# %% jupyter={"source_hidden": true}
-index_columns = ["local_segment", "local_segment_label", "local_segment_start_datetime", "local_segment_end_datetime"]
-#if "pid" in model_input.columns:
-#    index_columns.append("pid")
-model_input.set_index(index_columns, inplace=True)
-
-data_x, data_y, data_groups = model_input.drop(["target", "pid"], axis=1), model_input["target"], model_input["pid"]
-
-# %% jupyter={"source_hidden": true}
-categorical_feature_colnames = ["gender", "startlanguage"]
-additional_categorical_features = [col for col in data_x.columns if "mostcommonactivity" in col or "homelabel" in col]
-categorical_feature_colnames += additional_categorical_features
-
-# %% jupyter={"source_hidden": true}
-categorical_features = data_x[categorical_feature_colnames].copy()
-
-# %% jupyter={"source_hidden": true}
-mode_categorical_features = categorical_features.mode().iloc[0]
-
-# %% jupyter={"source_hidden": true}
-# fillna with mode
-categorical_features = categorical_features.fillna(mode_categorical_features)
-
-# %% jupyter={"source_hidden": true}
-# one-hot encoding
-categorical_features = categorical_features.apply(lambda col: col.astype("category"))
-if not categorical_features.empty:
-    categorical_features = pd.get_dummies(categorical_features)
-
-# %% jupyter={"source_hidden": true}
-numerical_features = data_x.drop(categorical_feature_colnames, axis=1)
-
-# %% jupyter={"source_hidden": true}
-train_x = pd.concat([numerical_features, categorical_features], axis=1)
-
-# %% jupyter={"source_hidden": true}
-train_x.dtypes
-
-# %% jupyter={"source_hidden": true}
-logo = LeaveOneGroupOut()
-logo.get_n_splits(
-    train_x,
-    data_y,
-    groups=data_groups,
-)
-
-# %% jupyter={"source_hidden": true}
-sum(data_y.isna())
-
-# %% [markdown]
-# ### Baseline: Dummy Regression (mean)
-dummy_regr = DummyRegressor(strategy="mean")
-
-# %% jupyter={"source_hidden": true}
-lin_reg_scores = cross_validate(
-    dummy_regr,
-    X=train_x,
-    y=data_y,
-    groups=data_groups,
-    cv=logo,
-    n_jobs=-1,
-    scoring=('r2', 'neg_mean_squared_error', 'neg_mean_absolute_error', 'neg_root_mean_squared_error')
-)
-print("Negative Mean Squared Error", np.median(lin_reg_scores['test_neg_mean_squared_error']))
-print("Negative Mean Absolute Error", np.median(lin_reg_scores['test_neg_mean_absolute_error']))
-print("Negative Root Mean Squared Error", np.median(lin_reg_scores['test_neg_root_mean_squared_error']))
-print("R2", np.median(lin_reg_scores['test_r2']))
-
-# %% [markdown]
-# ### Linear Regression
-
-# %% jupyter={"source_hidden": true}
-lin_reg_rapids = linear_model.LinearRegression()
-
-# %% jupyter={"source_hidden": true}
-lin_reg_scores = cross_validate(
-    lin_reg_rapids,
-    X=train_x,
-    y=data_y,
-    groups=data_groups,
-    cv=logo,
-    n_jobs=-1,
-    scoring=('r2', 'neg_mean_squared_error', 'neg_mean_absolute_error', 'neg_root_mean_squared_error')
-)
-print("Negative Mean Squared Error", np.median(lin_reg_scores['test_neg_mean_squared_error']))
-print("Negative Mean Absolute Error", np.median(lin_reg_scores['test_neg_mean_absolute_error']))
-print("Negative Root Mean Squared Error", np.median(lin_reg_scores['test_neg_root_mean_squared_error']))
-print("R2", np.median(lin_reg_scores['test_r2']))
-
-# %% [markdown]
-# ### XGBRegressor Linear Regression
-# %% jupyter={"source_hidden": true}
-xgb_r = xg.XGBRegressor(objective ='reg:squarederror', n_estimators = 10)
-
-# %% jupyter={"source_hidden": true}
-xgb_reg_scores = cross_validate(
-    xgb_r,
-    X=train_x,
-    y=data_y,
-    groups=data_groups,
-    cv=logo,
-    n_jobs=-1,
-    scoring=('r2', 'neg_mean_squared_error', 'neg_mean_absolute_error', 'neg_root_mean_squared_error')
-)
-print("Negative Mean Squared Error", np.median(xgb_reg_scores['test_neg_mean_squared_error']))
-print("Negative Mean Absolute Error", np.median(xgb_reg_scores['test_neg_mean_absolute_error']))
-print("Negative Root Mean Squared Error", np.median(xgb_reg_scores['test_neg_root_mean_squared_error']))
-print("R2", np.median(xgb_reg_scores['test_r2']))
-
-# %% [markdown]
-# ### XGBRegressor Pseudo Huber Error Regression
-# %% jupyter={"source_hidden": true}
-xgb_psuedo_huber_r = xg.XGBRegressor(objective ='reg:pseudohubererror', n_estimators = 10)
-
-# %% jupyter={"source_hidden": true}
-xgb_psuedo_huber_reg_scores = cross_validate(
-    xgb_psuedo_huber_r,
-    X=train_x,
-    y=data_y,
-    groups=data_groups,
-    cv=logo,
-    n_jobs=-1,
-    scoring=('r2', 'neg_mean_squared_error', 'neg_mean_absolute_error', 'neg_root_mean_squared_error')
-)
-print("Negative Mean Squared Error", np.median(xgb_psuedo_huber_reg_scores['test_neg_mean_squared_error']))
-print("Negative Mean Absolute Error", np.median(xgb_psuedo_huber_reg_scores['test_neg_mean_absolute_error']))
-print("Negative Root Mean Squared Error", np.median(xgb_psuedo_huber_reg_scores['test_neg_root_mean_squared_error']))
-print("R2", np.median(xgb_psuedo_huber_reg_scores['test_r2']))
-
-# %% [markdown]
-# ### Ridge regression
-
-# %% jupyter={"source_hidden": true}
-ridge_reg = linear_model.Ridge(alpha=.5)
-
-# %% tags=[] jupyter={"source_hidden": true}
-ridge_reg_scores = cross_validate(
-    ridge_reg,
-    X=train_x,
-    y=data_y,
-    groups=data_groups,
-    cv=logo,
-    n_jobs=-1,
-    scoring=('r2', 'neg_mean_squared_error', 'neg_mean_absolute_error', 'neg_root_mean_squared_error')
-)
-print("Negative Mean Squared Error", np.median(ridge_reg_scores['test_neg_mean_squared_error']))
-print("Negative Mean Absolute Error", np.median(ridge_reg_scores['test_neg_mean_absolute_error']))
-print("Negative Root Mean Squared Error", np.median(ridge_reg_scores['test_neg_root_mean_squared_error']))
-print("R2", np.median(ridge_reg_scores['test_r2']))
-
-# %% [markdown]
-# ### Lasso
-
-# %% jupyter={"source_hidden": true}
-lasso_reg = linear_model.Lasso(alpha=0.1)
-
-# %% jupyter={"source_hidden": true}
-lasso_reg_score = cross_validate(
-    lasso_reg,
-    X=train_x,
-    y=data_y,
-    groups=data_groups,
-    cv=logo,
-    n_jobs=-1,
-    scoring=('r2', 'neg_mean_squared_error', 'neg_mean_absolute_error', 'neg_root_mean_squared_error')
-)
-print("Negative Mean Squared Error", np.median(lasso_reg_score['test_neg_mean_squared_error']))
-print("Negative Mean Absolute Error", np.median(lasso_reg_score['test_neg_mean_absolute_error']))
-print("Negative Root Mean Squared Error", np.median(lasso_reg_score['test_neg_root_mean_squared_error']))
-print("R2", np.median(lasso_reg_score['test_r2']))
-
-# %% [markdown]
-# ### Bayesian Ridge
-
-# %% jupyter={"source_hidden": true}
-bayesian_ridge_reg = linear_model.BayesianRidge()
-
-# %% jupyter={"source_hidden": true}
-bayesian_ridge_reg_score = cross_validate(
-    bayesian_ridge_reg,
-    X=train_x,
-    y=data_y,
-    groups=data_groups,
-    cv=logo,
-    n_jobs=-1,
-    scoring=('r2', 'neg_mean_squared_error', 'neg_mean_absolute_error', 'neg_root_mean_squared_error')
-)
-print("Negative Mean Squared Error", np.median(bayesian_ridge_reg_score['test_neg_mean_squared_error']))
-print("Negative Mean Absolute Error", np.median(bayesian_ridge_reg_score['test_neg_mean_absolute_error']))
-print("Negative Root Mean Squared Error", np.median(bayesian_ridge_reg_score['test_neg_root_mean_squared_error']))
-print("R2", np.median(bayesian_ridge_reg_score['test_r2']))
-
-# %% [markdown]
-# ### RANSAC (outlier robust regression)
-
-# %% jupyter={"source_hidden": true}
-ransac_reg = linear_model.RANSACRegressor()
-
-# %% jupyter={"source_hidden": true}
-ransac_reg_scores = cross_validate(
-    ransac_reg,
-    X=train_x,
-    y=data_y,
-    groups=data_groups,
-    cv=logo,
-    n_jobs=-1,
-    scoring=('r2', 'neg_mean_squared_error', 'neg_mean_absolute_error', 'neg_root_mean_squared_error')
-)
-print("Negative Mean Squared Error", np.median(ransac_reg_scores['test_neg_mean_squared_error']))
-print("Negative Mean Absolute Error", np.median(ransac_reg_scores['test_neg_mean_absolute_error']))
-print("Negative Root Mean Squared Error", np.median(ransac_reg_scores['test_neg_root_mean_squared_error']))
-print("R2", np.median(ransac_reg_scores['test_r2']))
-
-# %% [markdown]
-# ### Support vector regression
-
-# %% jupyter={"source_hidden": true}
-svr = svm.SVR()
-
-# %% jupyter={"source_hidden": true}
-svr_scores = cross_validate(
-    svr,
-    X=train_x,
-    y=data_y,
-    groups=data_groups,
-    cv=logo,
-    n_jobs=-1,
-    scoring=('r2', 'neg_mean_squared_error', 'neg_mean_absolute_error', 'neg_root_mean_squared_error')
-)
-print("Negative Mean Squared Error", np.median(svr_scores['test_neg_mean_squared_error']))
-print("Negative Mean Absolute Error", np.median(svr_scores['test_neg_mean_absolute_error']))
-print("Negative Root Mean Squared Error", np.median(svr_scores['test_neg_root_mean_squared_error']))
-print("R2", np.median(svr_scores['test_r2']))
-
-# %% [markdown]
-# ### Kernel Ridge regression
-
-# %% jupyter={"source_hidden": true}
-kridge = kernel_ridge.KernelRidge()
-
-# %% jupyter={"source_hidden": true}
-kridge_scores = cross_validate(
-    kridge,
-    X=train_x,
-    y=data_y,
-    groups=data_groups,
-    cv=logo,
-    n_jobs=-1,
-    scoring=('r2', 'neg_mean_squared_error', 'neg_mean_absolute_error', 'neg_root_mean_squared_error')
-)
-print("Negative Mean Squared Error", np.median(kridge_scores['test_neg_mean_squared_error']))
-print("Negative Mean Absolute Error", np.median(kridge_scores['test_neg_mean_absolute_error']))
-print("Negative Root Mean Squared Error", np.median(kridge_scores['test_neg_root_mean_squared_error']))
-print("R2", np.median(kridge_scores['test_r2']))
-
-# %% [markdown]
-# ### Gaussian Process Regression
-
-# %% jupyter={"source_hidden": true}
-gpr = gaussian_process.GaussianProcessRegressor()
-
-# %% jupyter={"source_hidden": true}
-
-gpr_scores = cross_validate(
-    gpr,
-    X=train_x,
-    y=data_y,
-    groups=data_groups,
-    cv=logo,
-    n_jobs=-1,
-    scoring=('r2', 'neg_mean_squared_error', 'neg_mean_absolute_error', 'neg_root_mean_squared_error')
-)
-print("Negative Mean Squared Error", np.median(gpr_scores['test_neg_mean_squared_error']))
-print("Negative Mean Absolute Error", np.median(gpr_scores['test_neg_mean_absolute_error']))
-print("Negative Root Mean Squared Error", np.median(gpr_scores['test_neg_root_mean_squared_error']))
-print("R2", np.median(gpr_scores['test_r2']))
-
-# %%
diff --git a/exploration/ml_pipeline_daily_cleaned_intradaily.py b/exploration/ml_pipeline_regression.py
similarity index 97%
rename from exploration/ml_pipeline_daily_cleaned_intradaily.py
rename to exploration/ml_pipeline_regression.py
index dccdfd8..21d02cb 100644
--- a/exploration/ml_pipeline_daily_cleaned_intradaily.py
+++ b/exploration/ml_pipeline_regression.py
@@ -123,7 +123,7 @@ dummy_regr = DummyRegressor(strategy="mean")
 imputer = SimpleImputer(missing_values=np.nan, strategy='mean')
 
 # %% jupyter={"source_hidden": true}
-lin_reg_scores = cross_validate(
+dummy_regressor = cross_validate(
     dummy_regr,
     X=imputer.fit_transform(train_x),
     y=data_y,
@@ -132,10 +132,10 @@ lin_reg_scores = cross_validate(
     n_jobs=-1,
     scoring=('r2', 'neg_mean_squared_error', 'neg_mean_absolute_error', 'neg_root_mean_squared_error')
 )
-print("Negative Mean Squared Error", np.median(lin_reg_scores['test_neg_mean_squared_error']))
-print("Negative Mean Absolute Error", np.median(lin_reg_scores['test_neg_mean_absolute_error']))
-print("Negative Root Mean Squared Error", np.median(lin_reg_scores['test_neg_root_mean_squared_error']))
-print("R2", np.median(lin_reg_scores['test_r2']))
+print("Negative Mean Squared Error", np.median(dummy_regressor['test_neg_mean_squared_error']))
+print("Negative Mean Absolute Error", np.median(dummy_regressor['test_neg_mean_absolute_error']))
+print("Negative Root Mean Squared Error", np.median(dummy_regressor['test_neg_root_mean_squared_error']))
+print("R2", np.median(dummy_regressor['test_r2']))
 
 # %% [markdown]
 # ### Linear Regression