from sklearn.linear_model import LinearRegression
lin_reg = LinearRegression(normalize = True)
display_model_performance("Linear Regression",lin_reg)
ValueError Traceback (most recent call last)
~\AppData\Local\Temp/ipykernel_27260/2220606045.py in <module>
2
3 lin_reg = LinearRegression(normalize = True)
----> 4 display_model_performance("Linear Regression",lin_reg)
5
~\AppData\Local\Temp/ipykernel_27260/2396785647.py in display_model_performance(model_name, model, diamonds, labels, models_rmse, cvs_rmse_mean, tests_rmse, tests_accuracy, pipeline, x_test, y_test, cv)
44 print("--- Test Performance ---")
45
---> 46 x_test_prepared = pipeline.transform(x_test)
47
48 # Fit test dataset in model
C:\Users\Public\anaconda3\lib\site-packages\sklearn\compose\_column_transformer.py in transform(self, X)
717 """
718 check_is_fitted(self)
--> 719 X = _check_X(X)
720
721 fit_dataframe_and_transform_dataframe = hasattr(
C:\Users\Public\anaconda3\lib\site-packages\sklearn\compose\_column_transformer.py in _check_X(X)
818 if hasattr(X, "__array__") or sparse.issparse(X):
819 return X
--> 820 return check_array(X, force_all_finite="allow-nan", dtype=object)
821
822
C:\Users\Public\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)
767 # If input is 1D raise error
768 if array.ndim == 1:
--> 769 raise ValueError(
770 "Expected 2D array, got 1D array instead:\narray={}.\n"
771 "Reshape your data either using array.reshape(-1, 1) if "
ValueError: Expected 2D array, got 1D array instead:
array=[].
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.