RDA implementation compatible with Scikit-learn API
Model class is inherited from BaseEstimator
of scikit-learn
, so you can use the RDA
model just like the other scikit-learn
's esimators:
RDA
uses macro-f1 score as a score function.
rda_model = RDA()
rda_model.fit(X_train, y_train)
preds = rda_model.predict(X_test)
# Gridsearch CV
parameters = {'rda__alpha': np.linspace(0, 1.0, 11), 'rda__beta':np.linspace(0, 1.0, 11), 'rda__variance': [0.1, 0.5, 1.0]}
pipeline = Pipeline(
steps = [('scaler', StandardScaler()), ('rda', RDA())]
)
skf = StratifiedKFold(n_splits=3, shuffle=True, random_state=1)
rda_g_search = GridSearchCV(pipeline, parameters, cv=skf, n_jobs=-1, verbose=1)
rda_g_search.fit(X.to_numpy(), y.to_numpy())