sklearn Logistic Regression "ValueError: Found array with dim 3. Estimator expected <= 2."

I attempt to solve this problem 6 in this notebook. The question is to train a simple model on this data using 50, 100, 1000 and 5000 training samples by using the LogisticRegression model from sklearn.linear_model.

lr = LogisticRegression()
lr.fit(train_dataset,train_labels)

This is the code i trying to do and it give me the error.

ValueError: Found array with dim 3. Estimator expected <= 2.

Any idea?

UPDATE 1: Update the link to the Jupyter Notebook.

scikit-learn expects 2d num arrays for the training dataset for a fit function. The dataset you are passing in is a 3d array you need to reshape the array into a 2d.

nsamples, nx, ny = train_dataset.shape
d2_train_dataset = train_dataset.reshape((nsamples,nx*ny))

In LSTM, GRU, and TCN layers, the return_sequence in last layer before Dence Layer must set False . It is one of conditions that you encounter to this error message .

If anyone is stumbling onto this question from using LSTM or any RNN for two or more time series, this might be a solution.

However, to those who want error between two different values predicted, if for example you're trying to predict two completely different time series, then you can do the following:

from sklearn import mean_squared_error 
# Any sklearn function that takes 2D data only
# 3D data
real = np.array([
    [
        [1,60],
        [2,70],
        [3,80]
    ],
    [
        [2,70],
        [3,80],
        [4,90]
    ]
]) 

pred = np.array([
    [
        [1.1,62.1],
        [2.1,72.1],
        [3.1,82.1]
    ],
    [
        [2.1,72.1],
        [3.1,82.1],
        [4.1,92.1]
    ]
])

# Error/Some Metric on Feature 1:
print(mean_squared_error(real[:,:,0], pred[:,:,0]) # 0.1000

# Error/Some Metric on Feature 2:
print(mean_squared_error(real[:,:,1], pred[:,:,1]) # 2.0000

Additional Info from the numpy indexing