10  Exercise Solution: Regression with Trees (LOS Dataset)

If using colab, run this cell first. Otherwise, skip this cell.

!pip install ydata_profiling
!pip install catboost
!pip install --upgrade scikit-learn
!git clone https://github.com/hsma-programme/h6_4e_boosted_trees.git
%cd /content/h6_4e_boosted_trees/exercises_colab

10.1 Core

We’re going to work with a dataset to try to predict patient length of stay.

import pandas as pd
import matplotlib.pyplot as plt
%matplotlib inline

# import the relevant models from Sklearn, XGBoost, CatBoost and LightGBM
from sklearn.linear_model import LinearRegression
from sklearn.tree import DecisionTreeRegressor
from sklearn.ensemble import RandomForestRegressor
from ydata_profiling import ProfileReport

from sklearn.preprocessing import StandardScaler
from sklearn.model_selection import train_test_split

from xgboost import XGBRegressor
from lightgbm import LGBMRegressor
from catboost import CatBoostRegressor
from sklearn.ensemble import HistGradientBoostingRegressor
# import any other libraries you need
import numpy as np
from sklearn.metrics import mean_absolute_error, mean_absolute_percentage_error, \
                            r2_score, root_mean_squared_error

Open the data dictionary in the los_dataset folder and take a look at what data is available.

Next, load in the dataframe containing the LOS data.

los_df = pd.read_csv("../datasets/los_dataset/LengthOfStay.csv", index_col="eid")

View the dataframe.

los_df.head()

	vdate	rcount	gender	dialysisrenalendstage	asthma	irondef	pneum	substancedependence	psychologicaldisordermajor	depress	...	sodium	glucose	bloodureanitro	creatinine	bmi	pulse	respiration	secondarydiagnosisnonicd9	facid	lengthofstay
eid
1	8/29/2012	0	F	0	0	0	0	0	0	0	...	140.361132	192.476918	12.0	1.390722	30.432418	96	6.5	4	B	3
2	5/26/2012	5+	F	0	0	0	0	0	0	0	...	136.731692	94.078507	8.0	0.943164	28.460516	61	6.5	1	A	7
3	9/22/2012	1	F	0	0	0	0	0	0	0	...	133.058514	130.530524	12.0	1.065750	28.843812	64	6.5	2	B	3
4	8/9/2012	0	F	0	0	0	0	0	0	0	...	138.994023	163.377028	12.0	0.906862	27.959007	76	6.5	1	A	1
5	12/20/2012	0	F	0	0	0	1	0	1	0	...	138.634836	94.886654	11.5	1.242854	30.258927	67	5.6	2	E	4

5 rows × 26 columns

ProfileReport(los_df)