sklearn Logistic Regression "ValueError: Found array with dim 3. Estimator expected <= 2."
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.shaped2_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 datareal = 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