Reshape your data either using array.reshape(-1, 1) during model.predict()?

Question

I'm trying to run a number of classification models, but all of them keep throwing the reshape error. I think it has to do with the calculation of model.score or model.predict but i've tried running some reshape commands (on X_valid and Y_valid) with no success

Code:

X = train.drop("Survived", axis=1) # features
Y = train["Survived"] # target
X_test  = test # test set, containing no target

# run a split of train data and later predict on x_test
X_train, X_valid, Y_train, Y_valid = train_test_split(X, Y, random_state=42, test_size=0.20, stratify=Y)

# Random Forest

random_forest = RandomForestClassifier(n_estimators=100)
random_forest.fit(X_train, Y_train)

Y_pred = random_forest.predict(X_valid)

acc_random_forest = round(random_forest.score(Y_valid, Y_pred) * 100, 2)

Error and traceback:

---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
<timed exec> in <module>

C:\ProgramData\Anaconda3\lib\site-packages\sklearn\base.py in score(self, X, y, sample_weight)
    498         """
    499         from .metrics import accuracy_score
--> 500         return accuracy_score(y, self.predict(X), sample_weight=sample_weight)
    501 
    502     def _more_tags(self):

C:\ProgramData\Anaconda3\lib\site-packages\sklearn\ensemble\_forest.py in predict(self, X)
    628             The predicted classes.
    629         """
--> 630         proba = self.predict_proba(X)
    631 
    632         if self.n_outputs_ == 1:

C:\ProgramData\Anaconda3\lib\site-packages\sklearn\ensemble\_forest.py in predict_proba(self, X)
    672         check_is_fitted(self)
    673         # Check data
--> 674         X = self._validate_X_predict(X)
    675 
    676         # Assign chunk of trees to jobs

C:\ProgramData\Anaconda3\lib\site-packages\sklearn\ensemble\_forest.py in _validate_X_predict(self, X)
    420         check_is_fitted(self)
    421 
--> 422         return self.estimators_[0]._validate_X_predict(X, check_input=True)
    423 
    424     @property

C:\ProgramData\Anaconda3\lib\site-packages\sklearn\tree\_classes.py in _validate_X_predict(self, X, check_input)
    400         """Validate the training data on predict (probabilities)."""
    401         if check_input:
--> 402             X = self._validate_data(X, dtype=DTYPE, accept_sparse="csr",
    403                                     reset=False)
    404             if issparse(X) and (X.indices.dtype != np.intc or

C:\ProgramData\Anaconda3\lib\site-packages\sklearn\base.py in _validate_data(self, X, y, reset, validate_separately, **check_params)
    419             out = X
    420         elif isinstance(y, str) and y == 'no_validation':
--> 421             X = check_array(X, **check_params)
    422             out = X
    423         else:

C:\ProgramData\Anaconda3\lib\site-packages\sklearn\utils\validation.py in inner_f(*args, **kwargs)
     61             extra_args = len(args) - len(all_args)
     62             if extra_args <= 0:
---> 63                 return f(*args, **kwargs)
     64 
     65             # extra_args > 0

C:\ProgramData\Anaconda3\lib\site-packages\sklearn\utils\validation.py in check_array(array, accept_sparse, accept_large_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, ensure_min_samples, ensure_min_features, estimator)
    635             # If input is 1D raise error
    636             if array.ndim == 1:
--> 637                 raise ValueError(
    638                     "Expected 2D array, got 1D array instead:\narray={}.\n"
    639                     "Reshape your data either using array.reshape(-1, 1) if "

ValueError: Expected 2D array, got 1D array instead:
array=[0. 0. 1. 0. 1. 1. 1. 0. 0. 0. 0. 0. 1. 0. 0. 0. 0. 0. 0. 1. 0. 1. 0. 1.
 0. 1. 0. 1. 1. 0. 0. 1. 1. 0. 1. 1. 1. 0. 0. 1. 0. 0. 0. 1. 1. 0. 0. 0.
 1. 1. 1. 0. 0. 1. 1. 0. 0. 1. 1. 1. 0. 1. 0. 0. 1. 0. 1. 0. 0. 0. 1. 0.
 0. 1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1. 0. 1. 1. 0. 1. 0. 0. 1.
 1. 0. 0. 1. 0. 1. 1. 1. 0. 1. 0. 0. 0. 0. 0. 0. 1. 0. 1. 0. 0. 0. 0. 0.
 0. 1. 0. 0. 0. 0. 0. 0. 0. 1. 0. 0. 1. 0. 1. 0. 1. 0. 1. 1. 0. 0. 1. 0.
 0. 1. 0. 0. 1. 1. 1. 0. 1. 1. 0. 1. 0. 0. 0. 1. 1. 1. 1. 1. 1. 0. 0. 0.
 0. 1. 0. 0. 0. 0. 1. 0. 0. 1. 0.].
Reshape your data either using array.reshape(-1, 1) if your data has a single feature or array.reshape(1, -1) if it contains a single sample.

Y_valid seems to be the one causing the problem. I tried reshaping as follows:

Y_valid2 = Y_valid.values.reshape(-1,1)

random_forest = RandomForestClassifier(n_estimators=100)
random_forest.fit(X_train, Y_train)

Y_pred = random_forest.predict(X_valid)

acc_random_forest = round(random_forest.score(Y_valid2, Y_pred) * 100, 2)

But now a different error occurs:

ValueError: X has 1 features, but DecisionTreeClassifier is expecting 10 features as input.

I've tried viewing some other similar questions but I can't discover a successful correction for my own version of the issue. Help!

• Reshape your data either using array.reshape(-1, 1) if your data has a single feature or array.reshape(1, -1) if it contains a single sample
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