Decision Tree Regression

Installation

# To install the required package, use the following command:
# !pip install modeva

Authentication

# To get authentication, use the following command: (To get full access please replace the token to your own token)
# from modeva.utils.authenticate import authenticate
# authenticate(auth_code='eaaa4301-b140-484c-8e93-f9f633c8bacb')

Import required modules

from modeva import DataSet
from modeva import TestSuite
from modeva.models import MoDecisionTreeRegressor

Load and prepare dataset

ds = DataSet()
ds.load(name="BikeSharing")  # Changed dataset name
ds.set_random_split()
ds.set_target("cnt")

ds.scale_numerical(features=("cnt",), method="log1p")
ds.preprocess()

Train model

model = MoDecisionTreeRegressor(max_depth=3)
model.fit(ds.train_x, ds.train_y)

MoDecisionTreeRegressor(ccp_alpha=0.0, criterion='squared_error', max_depth=3,
                        max_features=None, max_leaf_nodes=None,
                        min_impurity_decrease=0.0, min_samples_leaf=1,
                        min_samples_split=2, min_weight_fraction_leaf=0.0,
                        monotonic_cst=None, random_state=None, splitter='best')

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Basic accuracy analysis

ts = TestSuite(ds, model)
results = ts.diagnose_accuracy_table()
results.table

	MSE	MAE	R2
train	0.555878	0.579652	0.723022
test	0.603628	0.600893	0.701683
GAP	0.047750	0.021241	-0.021339

Global tree interpretation

results = ts.interpret_global_tree()
results.plot()

Local tree interpretation

results = ts.interpret_local_tree(sample_index=0)
results.plot()