I trained a logistic regression model with some data. I applied standard scalar to train and test data, trained model. But if I want to make prediction with the model with the data outside the train and test data, I have to apply standard scalar to new data but what if I have single data than i cannot apply standard scalar to that new single sample that i want to give as input. What should be the procedure to predict results with new data especially single sample at a time?
1 Answers
The predict() method always expects a 2D array of shape [n_samples, n_features]. This means that if you want to predict even for a single data point, you will have to convert it into a 2D array.
Converting data into a 2D array using reshape
# Sample data
print(arr)
[1, 2, 3, 4]
# Reshaping into 2D
arr.reshape(1, -1)
# Result
array([[1, 2, 3, 4]])
This array can now be transformed using standard scalar using transform() method before being used to generate a prediction from the model.
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