Is it possible to predict using fewer number of features than the number of features that was used in training the model? I'm making a model using sklearn.svm.SVC that would predict machine performance (ErrorID). 

For the training of the model, I'm using 6 features, namely, EmployeeID, JobID, MachineID, Speed, RunningDateandTime, Meters and passing in ErrorID as labels.

Now for the prediction, I only have RunningDateandTime because I want to predict the performance in the future. But the model won't accept it because the number of features at the time of training and the number of features at the time of prediction is not same.

Is there any way to force the model to predict using only 1 feature while being trained on 5 features?
Machine learning model is nothing but an equation (hypothesis), derived from the features (data set) used for model training. Using training dataset we optimize this equation by changing the coefficient(also known as weight) values with each iteration(or with each batch).

For example:

 where x1, x2, x3 are features and theta1, theta2, theta3 are weights...that we have to optimize with each iteration.


  In order to get the results from this function, we need some value for
  x1, x2,and x3.  This function wont work in case you feed just either
  of these values.
  
  Best we can do in your case is to feed some average values for rest of
  the missing features. But then wont expect much from the model.