Decision Tree Classification

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 MoDecisionTreeClassifier

Load and prepare dataset

ds = DataSet()
ds.load(name="TaiwanCredit")  # Changed dataset name
ds.set_random_split()
ds.set_target("FlagDefault")

Train model

model = MoDecisionTreeClassifier(max_depth=3)  # Model initialization
model.fit(ds.train_x, ds.train_y)

MoDecisionTreeClassifier(ccp_alpha=0.0, class_weight=None, criterion='gini',
                         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

	AUC	ACC	F1	LogLoss	Precision	Recall	Brier
train	0.736758	0.820250	0.474543	0.444782	0.678509	0.364862	0.138957
test	0.733311	0.826333	0.473737	0.436493	0.686676	0.361604	0.135229
GAP	-0.003448	0.006083	-0.000806	-0.008289	0.008167	-0.003259	-0.003728

Global tree interpretation

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

Local tree interpretation

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