National Institute for Smart Government Interview Questions and Answers

Question 1

Q1. What is assembly evaluation app?

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Question 2

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Question 3

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Question 4

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Question 5

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Answer

Ensemble learning is a machine learning technique where multiple models are combined to improve the overall performance.

Ensemble learning helps to reduce overfitting and increase accuracy by combining the predictions of multiple models.
Examples of ensemble learning methods include Random Forest, Gradient Boosting, and AdaBoost.
Each model in an ensemble may use different algorithms or subsets of the training data to make predictions.

Question 6

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Answer

Boosting and bagging are both ensemble learning techniques used to improve the performance of machine learning models.

Boosting focuses on improving the performance of a single model by training multiple models sequentially, where each model corrects the errors of its predecessor.
Bagging, on the other hand, involves training multiple models independently and then combining their predictions through averaging or voting.
Boosting is more prone to overfitting compared to bagging.
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