Question: I want to follow up on this question: [What is the difference between a definition and a declaration?](https://stackoverflow.com/questions/1410563/what-is-the-difference-between-a-definition-and-a-declaration), there is answer about definition and a declaration, but nothing about implementation.

I want also know what is the difference between implementation and definition.

Answer: 
Perhaps you are looking for stacked classifiers.

In this approach, the predictions of earlier models are available as features for later models.

Look into [StackingClassifiers](https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.StackingClassifier.html).

Adapted from the documentation:

```
from sklearn.ensemble import StackingClassifier

estimators = [
     ('dtc_model', DecisionTreeClassifier()),
 ]

clf = StackingClassifier(
                estimators=estimators, 
                final_estimator=LGBMClassifier()
      )

```
As [@Ferdy](https://stackoverflow.com/a/70193071/4574633) explained in his post, there is no simple way to perform this operation and it is understandable.

Scikit-learn `DecisionTreeClassifier` takes only numerical features and cannot handle `nan` values whereas `LGBMClassifier` can handle those.

By looking at the decision function of `scikit-learn` you can see that all it can perform is splits based on `feature <= threshold`.

On the contrary `LGBM` can perform the following:

* `feature is na`
* `feature <= threshold`
* `feature in categories`

Splits in decision tree are selected at each step as they best splits the set of items. They try to minimize the node impurity (giny) or entropy.

The risk of further training a `DecisionTreeClassifier` is that you are not sure that splits performed in the original tree are the best, since you have new splits capabilities with `LGBM` that might/should lead in better performance.

I would recommend you to retrain the model with `LGBMClassifier` only as it might be possible that splits will be different from the original `scikit-learn` Tree.