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3 Machine Learning Quiz Questions with Answers explanation, Interview
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learning, ensemble machine learning methods, sequential ensemble methods, AdaBoost
Machine
learning Quiz Questions - Set 33
1. Which of the
following is/are true about ensemble methods?
a) Ensemble methods
can take the form of using different classifiers
b) Ensemble methods
are simple and cheap
c) For the data
from linear process, ensemble methods performs better than the linear models
d) Using same
classification algorithm with different settings is an ensemble method
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Ans : (a) and (d)
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Answer: (a) Ensemble methods can take the form of using
different classifiers and (d) Using same
classification algorithm with different settings is an ensemble method
Ensemble methods
can take the form of using different algorithms, using the same algorithm
with different settings, or assigning different parts of the dataset to
different classifiers.
Ensemble methods
- The learning algorithms which construct a set of classifiers and then
classify new data points by taking a choice of their predictions are known as
Ensemble methods. Random forest is an ensemble model where number of decision
trees is used to predict the output.
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2. Which of the following
is not an example of ensemble method?
a) AdaBoost
b) Decision tree
c) Random Forest
d) Bootstrapping
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Ans : (b)
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Answer: (b) Decision tree
Decision tree is
not an ensemble method. It is a single tree used for classification.
Random forest is
an ensemble model where we use multiple decision trees to predict outcomes.
AdaBoost is a
statistical classification meta-algorithm. It is called Adaptive Boosting as
the weights are re-assigned to each instance, with higher weights assigned to
incorrectly classified instances.
Bootstrapping generates
multiple bootstrap training sets from the original training set and uses each
of them to generate a classifier for inclusion in the ensemble.
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3. Which of the
following is an example of sequential ensemble model?
a) AdaBoost
b) Bootstrapping
c) Random forest
d) All of the above
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Ans : (a)
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Answer: (a) AdaBoost
AdaBoost is an
example of sequential ensemble model.
Boosting is an
ensemble technique that learns from previous predictor mistakes to make
better predictions in the future. The technique combines several weak base
learners to form one strong learner, thus significantly improving the
predictability of models. Boosting works by arranging weak learners in a
sequence, such that weak learners learn from the next learner in the sequence
to create better predictive models.
What is
sequential ensemble?
Sequential
ensemble: base learners are generated sequentially. The basic motivation of
sequential methods is to exploit the dependence between the base learners. Overall
performance may be improved by weighing previously mislabeled examples with
higher weight.
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Related links:
What is the difference between sequential and parallel ensemble methods?
What is sequential ensemble technique?
Why decision tree is not an ensemble method?
Why AdaBoost is a sequential type ensemble model?
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