I trained and saved a model that should predict a sons hight based on his fathers height. I then saved the model to Pickle. I can now load the model and want to use it but unfortunately a second variable is demanded from me (besides the height of the father) I think I did something wrong when training the model?

I will post the part of the code wher I think the error is in, please ask if you need more.

#Spliting the data into test and train data

#Doing a linear regression

#testing the model with an example value
TestValue = 65.0
filename = 'Father_Son_Height_Model.pckl'
loaded_model = pickle.load(open(filename, 'rb'))
result = loaded_model.predict(TestValue)

The error message says:

ValueError: Expected 2D array, got scalar array instead:


Reshape your data either using array.reshape(-1, 1) if your data has a single feature or array.reshape(1, -1) if it contains a single sample.

Thanks alot in advance.


1 Answer 1


You need to use loaded_model.predict(TestValue), not loaded_model.score(TestValue). The latter is for evaluating the models accuracy, and you would also need to pass the true height of the son, which is the y value it's asking for.

  • $\begingroup$ Thanks, makes sense, I changed it and the new error (edited in question) appears. Did I train the model wrong or something? Why is he expecting a array? $\endgroup$
    – G.M
    Sep 20, 2019 at 7:23
  • 1
    $\begingroup$ @G.M this is scikit-learn being a bit annoying. It needs an array in a specific shape or it won't work. Try loaded_model.predict(np.array(TestValue).reshape(-1, 1)). skl is a great package but it could be more robust to this kind of thing. Make sure to include import numpy as np somewhere $\endgroup$
    – Dan Scally
    Sep 20, 2019 at 7:29
  • $\begingroup$ Jesus Christ its working, i cant believe it :D I feel like Dr. Frankenstein now, thanks alot :D I guess this isnt necessary when there is more then one arguement given for testing? so for example if I would have height of father and mother since it would be an arrray already. $\endgroup$
    – G.M
    Sep 20, 2019 at 7:38
  • 1
    $\begingroup$ @G.M You're welcome. Yeah exactly; normally you'd be predicting against an array of multiple rows with multiple features and you'd not have hit that second error. $\endgroup$
    – Dan Scally
    Sep 20, 2019 at 7:42
  • $\begingroup$ You right I think scikit learn should consider that, it may be rare in the real world but for a learner I would assume it is commen issue to run into. And given python it I would assume its easyly avoidable.... But who cares, I made a machine lerning thingy :D $\endgroup$
    – G.M
    Sep 20, 2019 at 7:45

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