arvore de decisão

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  • #45435
    Andre Luiz
    Participante

      Boa tarde!! Deu erro, poderia me ajudar identificar, por favor,o problema?

       

      from yellowbrick.classifier import ConfusionMatrix
      cm = ConfusionMatrix(arvore_credit)
      cm.fit(x_census_treinamento,y_census_treinamento)
      cm.score(x_census_teste,y_census_teste)

       

      ValueError Traceback (most recent call last)
      ~\AppData\Local\Temp\ipykernel_10800\3205665905.py in <module>
      2 cm = ConfusionMatrix(arvore_credit)
      3 cm.fit(x_census_treinamento,y_census_treinamento)
      —-> 4 cm.score(x_census_teste,y_census_teste)

      ~\anaconda3\lib\site-packages\yellowbrick\classifier\confusion_matrix.py in score(self, X, y)
      192 “””
      193 # Call super to check if fitted and to compute self.score_
      –> 194 super(ConfusionMatrix, self).score(X, y)
      195
      196 # Create predictions from X (will raise not fitted error)

      ~\anaconda3\lib\site-packages\yellowbrick\classifier\base.py in score(self, X, y)
      236
      237 # This method implements ScoreVisualizer (do not call super).
      –> 238 self.score_ = self.estimator.score(X, y)
      239 return self.score_
      240

      ~\anaconda3\lib\site-packages\sklearn\base.py in score(self, X, y, sample_weight)
      649 from .metrics import accuracy_score
      650
      –> 651 return accuracy_score(y, self.predict(X), sample_weight=sample_weight)
      652
      653 def _more_tags(self):

      ~\anaconda3\lib\site-packages\sklearn\tree\_classes.py in predict(self, X, check_input)
      465 “””
      466 check_is_fitted(self)
      –> 467 X = self._validate_X_predict(X, check_input)
      468 proba = self.tree_.predict(X)
      469 n_samples = X.shape[0]

      ~\anaconda3\lib\site-packages\sklearn\tree\_classes.py in _validate_X_predict(self, X, check_input)
      431 “””Validate the training data on predict (probabilities).”””
      432 if check_input:
      –> 433 X = self._validate_data(X, dtype=DTYPE, accept_sparse=”csr”, reset=False)
      434 if issparse(X) and (
      435 X.indices.dtype != np.intc or X.indptr.dtype != np.intc

      ~\anaconda3\lib\site-packages\sklearn\base.py in _validate_data(self, X, y, reset, validate_separately, **check_params)
      583
      584 if not no_val_X and check_params.get(“ensure_2d”, True):
      –> 585 self._check_n_features(X, reset=reset)
      586
      587 return out

      ~\anaconda3\lib\site-packages\sklearn\base.py in _check_n_features(self, X, reset)
      398
      399 if n_features != self.n_features_in_:
      –> 400 raise ValueError(
      401 f”X has {n_features} features, but {self.__class__.__name__} ”
      402 f”is expecting {self.n_features_in_} features as input.”

      ValueError: X has 108 features, but DecisionTreeClassifier is expecting 3 features as input.

      In [ ]:

      #45438
      Denny Ceccon
      Moderador

        Olá André,

        Eu executei o notebook desta aula até essa parte do código e deu tudo certo, pela mensagem de erro é possível que você tenha esquecido de executar algum código, tenta de novo desde o início.

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