[Solved] What is ‘Overfitting’ in Machine learning?

Q.	What is ‘Overfitting’ in Machine learning?
A.	when a statistical model describes random error or noise instead of
B.	robots are programed so that they can perform the task based on data they gather from
C.	while involving the process of learning ‘overfitting’ occurs.
D.	a set of data is used to discover the potentially predictive relationship
Answer» A. when a statistical model describes random error or noise instead of

2.7k

Do you find this helpful?

View all MCQs in

Machine Learning (ML)

Discussion

No comments yet

Related MCQs

Which one of the following is suitable? 1. When the hypothsis space is richer, overfitting is more likely. 2. when the feature space is larger , overfitting is more likely.
Suppose you are training a linear regression model. Now consider these points.1. Overfitting is more likely if we have less data2. Overfitting is more likely when the hypothesis space is small.Which of the above statement(s) are correct?
What is Overfitting in Machine learning?
What is ‘Overfitting’ in Machine learning?
Machine learning techniques differ from statistical techniques in that machine learning methods
Which of the following are correct statement(s) about stacking? 1. A machine learning model is trained on predictions of multiple machine learning models 2. A Logistic regression will definitely work better in the second stage as compared to other classification methods 3. First stage models are trained on full / partial feature space of training data
Which of the following are correct statement(s) about stacking? A machine learning model is trained on predictions of multiple machine learning models A Logistic regression will definitely work better in the second stage as compared to other classification methods First stage models are trained on full / partial feature space of training data
Machine learning techniques differ from statistical techniques in that machine learning methods
In machine learning, an algorithm (or learning algorithm) is said to be unstable if a small change in training data cause the large change in the learned classifiers. True or False: Bagging of unstable classifiers is a good idea
Which of the following can help to reduce overfitting in an SVM classifier?