# Using Random Forest Regression correctly

I have run Random forest regression with python and I have the fear that I haven't done it correct.

I have original image that has 3 different bands (each pixels has 3 values) and I want to try to see if I can predict the first value using the two others.

For that I used Random Forest regression, I have created train and test and fit the model:

rf.fit(X_train,y_train)
rf_pred=rf.predict(X_test)


then after I checked the prediction on the test and saw it was good, I wanted to use the same model in order to predict all the values of all the pixels in the image, so I did that:

pred_all=rf.predict(data)


*data includes all the pixels of the image.

My question : is this the right way to do this? can I just predict all the pixel values just by using the rf.predict after fit it with the train and test sets? Or am I missing here some step that should be taken?