Assembling your first ensemble
It's time to build your first ensemble model! The Pokémon dataset from the previous exercise has been loaded and split into train and test sets.
Your job is to leverage the voting ensemble technique using the sklearn
API. It's up to you to instantiate the individual models and pass them as parameters to build your first voting classifier.
This exercise is part of the course
Ensemble Methods in Python
Exercise instructions
- Instantiate a
KNeighborsClassifier
calledclf_knn
with 5 neighbors (specified usingn_neighbors
). - Instantiate a
"balanced"
LogisticRegression
calledclf_lr
(specified usingclass_weight
). - Instantiate a
DecisionTreeClassifier
calledclf_dt
withmin_samples_leaf = 3
andmin_samples_split = 9
. - Build a
VotingClassifier
using the parameterestimators
to specify the following list of (str, estimator) tuples:'knn'
,clf_knn
,'lr'
,clf_lr
, and'dt'
,clf_dt
.
Hands-on interactive exercise
Have a go at this exercise by completing this sample code.
# Instantiate the individual models
clf_knn = ____
clf_lr = ____
clf_dt = ____(____, ____, random_state=500)
# Create and fit the voting classifier
clf_vote = ____(
estimators=[('____', ____), ('____', ____), ('____', ____)]
)
clf_vote.fit(X_train, y_train)