Multiple choices questions in Machine learning. Interview questions on machine learning, quiz questions for data scientist answers explained, Exam questions in machine learning, what is entropy, dataset with entropy zero is not good for learning, dataset with high entropy are good for learning in classification problem
Machine Learning MCQ - Zero entropy training examples are not good for learning in classification task
1. In a
classification problem, if
the entropy of a set of training examples is zero, then the training examples
are
a) good
for learning
b) not good for learning
c) good
for learning during training but not for testing
d) good for learning during testing but not for training
|
Answer: (b) not good for learning Entropy is a measure of uncertainty or randomness in
a dataset. When entropy becomes 0, then the dataset has no impurity. Datasets
with 0 impurities are not useful for learning because each example from the training
set belong to only one class.
What is zero entropy? For each training example, the model predicts a
single class with 100% certainty (probability = 1 for one class, and 0 for
all others). This implies that the model is perfectly confident about the
class of every training example.
A dataset with low entropy is more predictable and
easier to classify. The higher the
entropy, the harder it is to draw any conclusions from that information.
Low entropy means
less uncertain and high entropy means more uncertain. |
Related links:
What is entropy in machine learning?
Why entropy is important in classification machine learning?
Why zero entropy training data are not good for learning purpose?
High entropy is good for training whereas low entropy is not good for training
Machine learning solved mcq, machine learning solved mcq
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