Linear Regression (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 MoElasticNet

Load and prepare dataset

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

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

Execute the preprocessing steps defined above

Train model

model = MoElasticNet(name="GLM",
                     feature_names=ds.feature_names,
                     feature_types=ds.feature_types,
                     alpha=0.01)
model.fit(ds.train_x, ds.train_y)

MoElasticNet(alpha=0.01, copy_X=True, fit_intercept=True, l1_ratio=0.5,
             max_iter=1000, positive=False, precompute=False, random_state=None,
             selection='cyclic', tol=0.0001, warm_start=False)

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Basic accuracy analysis

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

	MSE	MAE	R2
train	1.026928	0.806320	0.488310
test	1.009832	0.798615	0.500934
GAP	-0.017097	-0.007705	0.012624

Coefficient interpretation

results = ts.interpret_coef(features=("season", "yr", "workingday", "weathersit"))
results.plot()

Feature importance

results = ts.interpret_fi()
results.plot()

Main effect plot

results = ts.interpret_effects(features="hr")
results.plot()

Local feature importance analysis

results = ts.interpret_local_fi(dataset="train",
                                sample_index=15,
                                centered=True)
results.plot()

Local feature importance with linear coefficients

results = ts.interpret_local_linear_fi(dataset="train",
                                       sample_index=15,
                                       centered=True)
results.plot()