Open PoojaPrasd opened 3 days ago
Dear Team,
I am running the titanic demo notebook and getting the below error in the code
skope_rules_clf.fit(X_train, y_train)
in SkopeRules.fit(self, X, y, sample_weight) 265 self._max_depths = self.max_depth \ 266 if isinstance(self.max_depth, Iterable) else [self.max_depth] 268 for max_depth in self._max_depths: --> 269 bagging_clf = BaggingClassifier( 270 base_estimator=DecisionTreeClassifier( 271 max_depth=max_depth, 272 max_features=self.max_features, 273 min_samples_split=self.min_samples_split), 274 n_estimators=self.n_estimators, 275 max_samples=self.maxsamples, 276 max_features=self.max_samples_features, 277 bootstrap=self.bootstrap, 278 bootstrap_features=self.bootstrap_features, 279 # oob_score=... XXX may be added 280 # if selection on tree perf needed. 281 # warm_start=... XXX may be added to increase computation perf. 282 n_jobs=self.n_jobs, 283 random_state=self.random_state, 284 verbose=self.verbose) 286 bagging_reg = BaggingRegressor( 287 base_estimator=DecisionTreeRegressor( 288 max_depth=max_depth, (...) 300 random_state=self.random_state, 301 verbose=self.verbose) 303 clfs.append(bagging_clf)
TypeError: BaggingClassifier.init() got an unexpected keyword argument 'base_estimator'
Dear Team,
I am running the titanic demo notebook and getting the below error in the code
skope_rules_clf.fit(X_train, y_train)
in SkopeRules.fit(self, X, y, sample_weight) 265 self._max_depths = self.max_depth \ 266 if isinstance(self.max_depth, Iterable) else [self.max_depth] 268 for max_depth in self._max_depths: --> 269 bagging_clf = BaggingClassifier( 270 base_estimator=DecisionTreeClassifier( 271 max_depth=max_depth, 272 max_features=self.max_features, 273 min_samples_split=self.min_samples_split), 274 n_estimators=self.n_estimators, 275 max_samples=self.maxsamples, 276 max_features=self.max_samples_features, 277 bootstrap=self.bootstrap, 278 bootstrap_features=self.bootstrap_features, 279 # oob_score=... XXX may be added 280 # if selection on tree perf needed. 281 # warm_start=... XXX may be added to increase computation perf. 282 n_jobs=self.n_jobs, 283 random_state=self.random_state, 284 verbose=self.verbose) 286 bagging_reg = BaggingRegressor( 287 base_estimator=DecisionTreeRegressor( 288 max_depth=max_depth, (...) 300 random_state=self.random_state, 301 verbose=self.verbose) 303 clfs.append(bagging_clf)
TypeError: BaggingClassifier.init() got an unexpected keyword argument 'base_estimator'