How to use inverse_transform in MinMaxScaler for pred answer in a matrix
From my understanding you are working on a regression task in which you have applied MainMaxScaler to your target variable y prior modeling.
If so you have two options:

As the error message suggests, you can reshape the output with array.reshape(-1, 1)

Scikit learn has implemented a class to work with transformations on target:


So just try
from sklearn.svm import SVR
from sklearn.compose import TransformedTargetRegressor
from sklearn.metrics import r2_score
from sklearn.preprocessing import MinMaxScaler

regressor = SVR(kernel = 'linear')



model = TransformedTargetRegressor(regressor= regressor,
                                        transformer = MinMaxScaler()
                                        ).fit(trainX,trainY)

pred = model.predict(testX)
print(pred)

Every time you make a model.predict(X) it will apply an inverse transformation so that your predictions are in the same scale as prior MinMaxScaler
EDIT:
Working example of transformation without using Scikit-learn
# array example is between 0 and 1
array = np.array([0.58439621, 0.81262134, 0.231262134, 0.191])
#scaled from 100 to 250
minimo = 100
maximo = 250
array * minimo + (maximo - minimo)

Returns:
array([208.439621 , 231.262134 , 173.1262134, 169.1])