Use your model to predict values on holdout data

You're ready to make some predictions! Remember, the train-test-split you've carried out so far is for model development. The original competition provides an additional test set, for which you'll never actually see the correct labels. This is called the "holdout data."

The point of the holdout data is to provide a fair test for machine learning competitions. If the labels aren't known by anyone but DataCamp, DrivenData, or whoever is hosting the competition, you can be sure that no one submits a mere copy of labels to artificially pump up the performance on their model.

Remember that the original goal is to predict the probability of each label. In this exercise you'll do just that by using the .predict_proba() method on your trained model.

First, however, you'll need to load the holdout data, which is available in the workspace as the file HoldoutData.csv.

Read HoldoutData.csv into a DataFrame called holdout. Specify the keyword argument index_col=0 in your call to read_csv().
Generate predictions using .predict_proba() on the numeric columns (available in the NUMERIC_COLUMNS list) of holdout. Make sure to fill in missing values with -1000!

Take Hint (-30 XP)

Exercise

Use your model to predict values on holdout data

Instructions