MACHINE LEARNING

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APPLICATION OF SUPERVISED LEARNING

UNSUPERVISED LEARNING

Question [CLICK ON ANY CHOICE TO KNOW THE RIGHT ANSWER]
True or False. The larger the number of clusters the less likely the model is to overfit
A
True
B
False
C
Either A or B
D
None of the above
Explanation: 

Detailed explanation-1: -Your model is overfitting your training data when you see that the model performs well on the training data but does not perform well on the evaluation data. This is because the model is memorizing the data it has seen and is unable to generalize to unseen examples.

Detailed explanation-2: -When the feature space is larger, overfitting is less likely. False. The more the number of features, the higher the complexity of the model and hence greater its ability to overfit the training data. 3.

Detailed explanation-3: -Overfitting is when the model’s error on the training set (i.e. during training) is very low but then, the model’s error on the test set (i.e. unseen samples) is large! Underfitting is when the model’s error on both the training and test sets (i.e. during training and testing) is very high.

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