SKlearn classifier's predict_proba doesn't sum to 1

I have a classifier (in this case, it is the sklearn.MLPClassifier), with which I'm trying to perform classification into one of 18 classes.

The class is thus multi-class, not multi-label. I'm trying to predict only a single class.

I have my training data: X X.shape = (103393, 300) and Y Y.shape = (103393, 18), where the target Y is a one-hot encoded vetctor, denoting the target class.

> EDIT in response to @Dr. Snoopy: I do not supply any labels -- I simply pass the 18-dimensional vector with the corret class' index corresponding to the 1 in the vector, and all others being 0 (One hot encoded vector).
> To prove that the vectors are correctly 1-hot encoded, I can run

import pandas as pd
pd.DataFrame(Y.sum(axis=1)).value_counts()

>This returns 103393 counts of 1. Vectors are correctly 1-hot encoded, even upon examination.

When I fit the model, and return the class probability for all classes, the probability vector does not sum up to 1. Why might that be?

Here is an example of how I run the fitting:

from sklearn.neural_network import MLPClassifier

X_train, Y_train, X_test, Y_test = get_data()

model = MLPClassifier(max_iter=10000)
model.fit(X_train,Y_train)
probability_vector = model.predict_proba(X_test[0, :].respahe(1,-1))

Some of the time, the outputs are pretty close to 1. I suspect the error is probably due to rounding.

In other cases, the outputs sum to ~0.5 or less. Example output:

probability_vector = list(model.predict_proba(X_test[301,:].reshape(1,-1))[0])
print(probability_vector)
>>> [1.7591416e-06,
 3.148203e-05,
 3.9732524e-05,
 0.3810972,
 0.059248358,
 0.00032832936,
 8.5996935e-06,
 9.0914684e-05,
 9.377927e-07,
 0.0007674346,
 1.5543707e-06,
 0.0008467222,
 0.009655427,
 2.5728454e-05,
 1.07812774e-07,
 0.00022920035,
 0.00050288404,
 0.013878004]

len(probability_vecto)

>>> 18

sum(probability_vector)
>>> 0.46675437349917814

Why might this be happening? Is my model initialized incorrectly?

>
>
>
> Note: A couple of possible reasons for the error & my comments on them:
>
> - Class imbalance: The classes in the dataset are indeed, imbalanced. However, the non-1 summation problem is happening in well represented classes too, not just the underrepresented ones. Could this be a consequence of a model, which is not expressive enough?
>
> - Model uncertainty: "The model may not have a high level of confidence in its predictions for every input. " Is that all it is?

Do not one-hot encode your Y. The MLP classifier will do that for you using LabelBinarizer and then it will apply the softmax function correctly.
With multiple dimensions it will do multi-label classification.

Some explaining is done in the docs

You can check this for example by accessing model.out_activation_ or LabelBinarizer().fit(Y).y_type_ which should be softmax/multiclass , but here it will be logistic/multilabel-indicator

What you get at the moment are the logistic outputs of the individual classes.