Lazy Learners
June 15, 2023
Lazy Learners
lazy learning is a learning method in which generalization of the training data is, in theory, delayed until a query is made to the system, as opposed to eager learning, where the system tries to generalize the training data before receiving queries.
Lazy learning refers to machine learning processes in which generalization of the training data is delayed until a query is made to the system. This type of learning is also known as Instance-based Learning.
Lazy classifiers are very useful when working with large datasets that have a few attributes.
Learning systems have computation occurring at two different times: training time and consultation times.

Training time is the time before the consultation time. During training time, the system derives inferences from training data to prepare for the consultation time.
Instance-based learning, local regression, K-Nearest Neighbors (K-NN), and Lazy Bayesian Rules are some examples of lazy learning.

What are the advantages of a lazy learning algorithm?
Here are the most significant advantages of the lazy learning method:
• It is very useful when not all the examples are priorly available, but need to be
collected online. In such a situation, a new example observed would only require an
update to the database.
• In lazy learning, collecting examples about an operating regime does not degrade
the modeling performance of other operating regimes. Essentially, lazy learning is
not prone to suffering from data interference.
• The problem-solving capabilities of a lazy learning algorithm increase with every
newly presented case.
• Lazy learning is easy to maintain because the learner will adapt automatically to
changes in the problem domain.
• They can be simultaneously applied to multiple problems.

What are the disadvantages of a lazy learning algorithm?
Here are the most significant disadvantages of lazy learning:
• It is possible that a vast amount of memory would be needed to store the data,
especially because every single request for information would require the system to
start the identification of a local model from scratch. In practice, however, this
does not tend to be an issue, due to the advances in hardware and the smaller number
of attributes that need to be stored.
• Lazy learning methods tend to be slower to evaluate, however, this could be set
off by the quick training phase.
• If the data is rather noisy, then the case base gets pointlessly increased. This
is due to the fact that the algorithm does not make any abstraction during the
training phase since (as mentioned earlier) if learning is done in advance, it
becomes obsolete rather soon.
• Lazy learning tends to increase your costs. There is a fixed amount of
computational cost because the processor will only be able to process a limited
amount of training data points.
• In Case-Based Reasoning, handling very dynamic problem domains involves
reorganizing the case base on a continuous basis. This could cause errors to be
introduced in the case base. The set previously encountered examples could become
outdated if there is a sudden large shift in the problem domain.
• A lazy learner will only be able to achieve fully automatic operation for complete
problem domains. If the problem domain is not complete, there will be a requirement
for user feedback for situations in which the learner has no solution.
Interview Questions :
1. What are Lazy Learners?
2. What are the advantages of lazy learners algorithm?
3. What are the disadvantages of lazy learners algorithm?
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