Suppose you are using stacking with n different machine learning algorithms with k folds on data. Which of the following is true about one level (m base models + 1 stacker) stacking? Note: Here, we are working on binary classification problem All base models are trained on all features You are using k folds for base models

The correct answer is: C. you will have k+m features after the first stage

Stacking is a machine learning technique that combines the predictions of multiple base models to produce a more accurate prediction. In one level stacking, the base models are trained on the original features, and the predictions of the base models are then used as features for the stacker model. The stacker model is then trained on the predictions of the base models to produce a final prediction.

In the given question, we are using n different machine learning algorithms with k folds on data. This means that we will have n base models, each of which is trained on a different subset of the data. The predictions of the base models will then be used as features for the stacker model. The stacker model will be trained on the predictions of the base models to produce a final prediction.

After the first stage, we will have k+m features. This is because we will have the original features, as well as the predictions of the base models. The predictions of the base models will be used as features for the stacker model.

Here is a diagram that illustrates the process of stacking:

The diagram shows that the original features are used to train the base models. The predictions of the base models are then used as features for the stacker model. The stacker model is then trained on the predictions of the base models to produce a final prediction.

Here is a table that summarizes the different options:

| Option | Description |
|—|—|
| A | You will have only k features after the first stage. This is incorrect because we will have k+m features after the first stage. |
| B | You will have only m features after the first stage. This is incorrect because we will have k+m features after the first stage. |
| C | You will have k+m features after the first stage. This is correct because we will have the original features, as well as the predictions of the base models. The predictions of the base models will be used as features for the stacker model. |
| D | You will have k*n features after the first stage. This is incorrect because we will have k+m features after the first stage. |