okay I’ll try again with better formatting this time:
Hey, it’s me again! Your input really helped and I just quickly wanted to run this by to see whether what I’m doing is as it is intended: I have one .csv that i split into training and testing, and another .csv that i want to use purely as testing set to compare with the first. I started out as per your beginner tutorial with this
model_1 = tfdf.keras.RandomForestModel(
    compute_oob_variable_importances=True,
    max_depth = 20,
    num_trees = 250
    )

model_1.compile(
    metrics=["accuracy", tf.keras.metrics.Recall(), tf.keras.metrics.Precision(), tf.keras.metrics.FalseNegatives(), tf.keras.metrics.FalsePositives()])

with sys_pipes():
  model_1.fit(x=train_ds)

and for the second pure test set did this:
test_ds_pd2 = dataset_df1
train_ds2 = tfdf.keras.pd_dataframe_to_tf_dataset(train_ds_pd, label=label)
test_ds2 = tfdf.keras.pd_dataframe_to_tf_dataset(test_ds_pd2, label=label)

model_2 = tfdf.keras.RandomForestModel(
compute_oob_variable_importances=True, 
 max_depth = 20,
num_trees = 250
  )

    model_2.compile(
        metrics=["accuracy", tf.keras.metrics.Recall(), tf.keras.metrics.Precision(), tf.keras.metrics.FalseNegatives(), tf.keras.metrics.FalsePositives()])

    with sys_pipes():
      model_2.fit(x=train_ds)

does that make sense? I apologize for this very basic question, just want to ensure I got it correct