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DataFeud Round 1

Authored by Sparsh Shah

Computers

University

Used 2+ times

DataFeud Round 1
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20 questions

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1.

MULTIPLE CHOICE QUESTION

30 sec • 1 pt

Which ensemble learning techniques combines multiple decision tress to improve predictive performance?

K-nearest neighbours

Support vector machines

Random forest

Logistic regression

2.

MULTIPLE CHOICE QUESTION

30 sec • 1 pt

K- means clustering is a which type of machine learning algorithm?

Semi-supervised Machine learning

Unsupervised Machine learning

Supervised Machine learning

Reinforcement Machine learning

3.

MULTIPLE CHOICE QUESTION

30 sec • 1 pt

Which of the following is a supervised learning task?

Clustering

Dimensionality Reduction

  • Regression

  • Association

4.

MULTIPLE CHOICE QUESTION

30 sec • 1 pt

Regularization techniques are primarily used to prevent:

Underfitting

Overfitting

Bias

Variance

5.

MULTIPLE CHOICE QUESTION

30 sec • 1 pt

Dropout is a regularization technique used to prevent overfitting in neural networks.True or False?

TRUE

FALSE

6.

MULTIPLE CHOICE QUESTION

30 sec • 1 pt

Which type of LLM is OpenAI’s GPT series an example of?

Autoencoding Language Models

Autoregressive Language Models

Combination of both

None of the above

7.

MULTIPLE CHOICE QUESTION

30 sec • 1 pt

What is "Deep Learning" primarily concerned with?

Shallow and simple models

Neural networks with many layers

Quick learning algorithms

Deep sea data exploration

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