Multiple choices questions in Machine learning. Interview questions on machine learning, quiz questions for data scientist answers explained, Exam questions in machine learning, time complexities of different ML algorithms, why knn has no training phase, which ML algorithm have lowest training time?
Machine Learning MCQ - Which Machine Learning algorithm have lowest training time for very large datasets?
1. For very large
training data sets, which of the following will usually have the lowest
training time?
a) Logistic regression
b) Neural nets
c) K-Nearest Neighbors
d) Random forests
e) Linear SVM
|
Answer: (c) K-Nearest Neighbors K-Nearest Neighbors (KNN) is often referred to as a "lazy
learner" because it does not have a conventional training phase. Instead
of learning parameters (like weights in logistic regression or neural
networks), KNN stores the entire training dataset during the training phase.
Why
KNN does not have training phase or lowest training time? Since KNN does
not involve fitting a model or optimizing any parameters during training, it
does not require any significant computation
or model-building steps before predictions. In other words, it does
not learn or build a model in advance. This is why we say it has no training time.
Why not
other options? Time complexities
of other machine learning algorithms are as follows;
Linear SVM – O(n*p) Random Forest – O(n*p*log
n) Neural nets (complexity
per iteration) – O(n*p*h) Logistic
regression – O(n*p) Here, n refers to
number of training samples, p refers to the number of features, h refers to
the number of hidden units in a neural net. |
Related links:
What is the training time complexities of various machine learning algorithms?
Which ML algorithm(s) have lowest training time for very large datasets?
Why knn does have zero training time complexity?
Why does the norm of the weight vector grows in soft-margin SVM due to the increase in regularization parameter C?
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