[Python] How to input a matrix and receive value (or array of values) using machine learning...

I have a bunch of binary 8x15 matrixes and I want to determine which index (row and column) of the matrix breaks the pattern.

Say I have the following matrix (simplified to 5x5 for ease, because the problem remains the same):

1 1 1 1 0
1 1 1 1 0
1 1 0 1 0
1 1 1 0 0
1 1 1 0 0


This matrix should return [3, 3], because that position breaks the pattern of filling the matrix column by column with 1.

The following example should return [3, 1]:

1 1 1 1 0
1 1 1 1 0
0 1 1 0 0
1 1 1 0 0
1 1 1 0 0


The following example, however, doesn't show that problem, so it should return [-1, -1] or something (suggestions?):

1 1 1 1 0
1 1 1 1 0
1 1 1 1 0
1 1 1 1 0
1 1 1 0 0


How should I approach this problem using machine learning?

Currently, I have a pandas dataframe with 2 columns: the first one contains the matrix (so an array of 8 arrays of 15 positions; therefore, 8x15 matrix), while the second one contains the expected return / result:

State Result
0 [[0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1]... [-1, -1]
1 [[1, 0, 1, 0, 1, 1, 0, 1, 1, 0, 0, 1, 0, 0, 1]... [-1, -1]
2 [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]... [-1, -1]
3 [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]... [-1, -1]
4 [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]... [-1, -1]


dtypes:

State object
Result object
dtype: object


So one entry of the dataframe above would have the first column with a value like:

[[1, 1, 1, 1, 1, 0, 0, 0],
[1, 1, 1, 1, 1, 0, 0, 0],
[1, 1, 1, 1, 1, 0, 0, 0],
[1, 1, 1, 1, 0, 0, 0, 0],
[1, 1, 1, 1, 0, 0, 0, 0],
[1, 1, 0, 1, 0, 0, 0, 0],
[1, 1, 1, 1, 0, 0, 0, 0],
[1, 1, 1, 1, 0, 0, 0, 0]]


And its second column would have the value [6, 3].

Then I split it in 2 dataframes, X and y:

X = states_data.drop(columns=['Result'])
y = states_data[['Result']]


After that, I tried to use DecisionTreeClassifier with sklearn to solve the problem:

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2)
X_train = X_train.reset_index(drop=True)
X_test = X_test.reset_index(drop=True)
y_train = y_train.reset_index(drop=True)
y_test = y_test.reset_index(drop=True)

model = DecisionTreeClassifier()
model.fit(X_train, y_train)
predictions = model.predict(X_test)
predictions = pd.DataFrame(predictions, columns = ['Result'])

score = accuracy_score(y_test['Result'], predictions['Result'])
print(score)


However, in this line:

model.fit(X_train, y_train)


I get this error:

TypeError: float() argument must be a string or a number, not 'list'


Can someone help me? What am I doing wrong?

Some additional information:

X_train.dtypes returns:

State object
dtype: object


y_train.dtypes returns:

Result object
dtype: object


Both X_train.shape and y_train.shape return:

(543, 1)

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