我已经有训练和测试数据集，如何将它们传递给 model

Question

Usually we have one datset and we perform train and test split, but now i already have two datasets ie train data set and test data set. How do i pass them to the model??

Answer 1

I am assuming your train dataset is the one with the labels and your test dataset is the one that is close to the real world data that you need to predict on. So you need to use your train data like you would typically, perform EDA etc. You can still split the train data into a 80-20 split or similar and validate the model.

Once the model is trained you can predict on the test. Since your test may not have labels, you will not get any metrics. All evaluation is done on the validation set.

 X_train, X_val, y_train, y_val = train_test_split(X,y,test_size = 0.2)

   model = RanndomForestClassifier() # instantiate model
   model.fit(X_train, y_train) # fit on the train data
   model.predict(X_val)  # predict on the validation set to measure performance 
   model.predict(test) # predict on the test set

Answer 2

I'm going to assume that you're using keras for this and have already made your model.

Since you've already split your datasets, you can just go ahead and train your model on the training sets like this:

model.fit(x_train, y_train, batch_size = 64, epochs = 10)

Then once you want to use your training set, just run:

model.evaluate(x_test, y_test, batch_size = 128)

If you aren't using keras then let me know and we can work from there.