ValueError: Expected 2D array, got 1D array instead insists after converting 1D array to 2D

Question

I looked into other answers but still cannot understand why the problem insists.

Classic machine learning practice with Iris dataset.

Code:

dataset=load_iris()

X = np.array(dataset.data)
y = np.array(dataset.target)

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3)

model = KNeighborsClassifier()
model.fit(X_train, y_train)

prediction=model.predict(X_test)

The shapes of all arrays:

X shape: (150, 4)
y shape: (150,)
X_train: (105, 4)
X_test: (45, 4)
y_train: (105,)
y_test (45,)
prediction: (45,)

Trying to print this model.score(y_test, prediction) and I get the error. I tried to convert y_test and prediction into 2D arrays by using .reshape(-1,1) and I get another error: query data dimension must match training data dimension.

It's not only about the solution, it's about understanding what's wrong.

NickHilton · Accepted Answer · 2019-08-07 22:28:32Z

0

Its often useful to look at the signature and docstrings for functions you use. model.score for example has

Parameters
----------
X : array-like, shape = (n_samples, n_features)
    Test samples.

y : array-like, shape = (n_samples) or (n_samples, n_outputs)
    True labels for X.

in the docstrings to show you the exact type of input you should give it.

Here you would do model.score(X_test, y_test)

model.score will do both the prediction from X_test and the comparison with y_test

answered Aug 7, 2019 at 22:28

NickHilton

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1 Comment

Efi Kir Over a year ago

What an unexpected problem I had. My mind was miles away. Thanks!

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