MACHINE LEARNING

MACHINE LEARNING

INTRODUCTION

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Question [CLICK ON ANY CHOICE TO KNOW THE RIGHT ANSWER]

You trained a binary classifier model which gives very high accuracy on the training data, but much lower accuracy on validation data. Which of the following may be true?

A	This is an instance of overfitting.
B	This is an instance of underfitting.
C	The training was not well regularized.
D	The training and testing examples are sampled from different distributions.

Explanation:

Detailed explanation-1: -You trained a binary classifier model which gives very high accuracy on the training data, but much lower accuracy on validation data. Which of the following may be true? This is an instance of overfitting.

Detailed explanation-2: -Explanation: A model that yields high training accuracy but low out-of-sample accuracy is experiencing overfitting. Overfitting occurs when a model is too closely fit to the training data, resulting in poor generalization to new, unseen data.

Detailed explanation-3: -Even when model fails to predict any Crashes its accuracy is still 90%. As data contain 90% Landed Safely. So, accuracy does not holds good for imbalanced data. In business scenarios, most data won’t be balanced and so accuracy becomes poor measure of evaluation for our classification model.

There is 1 question to complete.