Building the second-layer classifier

Now you'll work on the next two steps of the process.

Step 3: Append the predictions to the dataset The predictions with the first-layer estimators were already calculated and are available to you as: pred_dt and pred_knn. You'll use these to create a DataFrame and append them to the training features.

Step 4: Build the second-layer meta estimator For this purpose you'll build the default DecisionTreeClassifier. This will take as input feature the concatenation of the original training features and the predictions from the first-layer estimators.

Create a DataFrame with the predictions from the first-layer estimators: pred_dt and pred_knn.
Concatenate the training features set with the predictions DataFrame.
Build and fit the second-layer meta estimator: a DecisionTreeClassifier with random_state = 500.

Take Hint (-30 XP)

Exercise

Building the second-layer classifier

Instructions