How can you adjust a prediction based on features in the future being different than predicted?

Question

I have a model that takes mostly cumulative data, and makes a prediction. It's not baseball, but I'm using this as a pretty accurate analogy. You put in all the totals so far, and it make a prediction of where a target will be in the future.

games_played|runs|runs_against|hits|hits_against|games_won
          20| 100|         120| 200|         240|       10

Expected games won after 5 more games: 12

What I'd like to do is answer questions like "How many runs are we expected to have in N games? And if we had more runs than that, how many more wins would that get us?"

             |games_played|runs|runs_against|hits|hits_against|games_won
          now|          20| 100|         120| 200|         240|       10
    predicted|          25| 120|         150| 240|         300|       12
user_adjusted|          25| 130|         140| 260|         280|       13

The only way I can think of to do this right now is to have many models, one for each feature, and put all the predicted features into one other model. Then users can update individual features. That feels like excess work and prone to errors, but I could be totally wrong. I can't just linearly forecast the features in the future because the relationship is more complex.

What is the algorithm/method/tool that's best suited to this? I'm using scikit-learn right now.

Answer 1

Try these two approaches:

First:

Make a model, using any ML algorithm and divide your data into train and test. Now using the previous features, check the train and test accuracy.

Now add the new features to the previous ones, again divide data into train and test. Check the train and test accuracy.

If the new features help improve the test accuracy then only use them otherwise just skip them.

Second:

Add the new features to the previous dataset and set their values as null/0. So while predicting the results before wont be affected by their values. And later when these features do get values, they will help improve the predictions.

I hope this helps!