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Understanding KNN and Classification

Authored by Neerja Negi

English

University

Used 1+ times

Understanding KNN and Classification
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20 questions

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1.

MULTIPLE CHOICE QUESTION

30 sec • 1 pt

Explain the basic principle behind the KNN algorithm.

KNN predicts data points based on a linear regression model.

KNN uses a decision tree to classify data points.

KNN requires labeled data to be clustered before classification.

KNN classifies data points based on the majority class of their nearest neighbors.

2.

MULTIPLE CHOICE QUESTION

30 sec • 1 pt

How does KNN classify a new data point?

KNN uses a decision tree to classify new data points.

KNN assigns a class based on the distance from the origin of the dataset.

KNN classifies a new data point by majority voting among its k nearest neighbors.

KNN classifies a new data point by averaging the values of all data points.

3.

MULTIPLE CHOICE QUESTION

30 sec • 1 pt

List one advantage of using KNN for classification tasks.

It is the fastest algorithm for all datasets.

It requires a large amount of memory.

It is easy to implement and understand.

It can only be used for binary classification.

4.

MULTIPLE CHOICE QUESTION

30 sec • 1 pt

What is a potential drawback of using KNN?

High computational cost with large datasets.

KNN requires extensive preprocessing of data.

KNN is the fastest algorithm for all datasets.

KNN is always accurate regardless of data size.

5.

MULTIPLE CHOICE QUESTION

30 sec • 1 pt

Identify a scenario where KNN would be an appropriate algorithm to use.

A decision tree for classifying images based on pixel values.

A clustering algorithm for grouping similar documents without labels.

A recommendation system for predicting user preferences based on similar users' behaviors.

A linear regression model for predicting continuous values.

6.

MULTIPLE CHOICE QUESTION

30 sec • 1 pt

What type of learning is KNN associated with?

Reinforcement learning

Unsupervised learning

Instance-based learning

Supervised learning

7.

MULTIPLE CHOICE QUESTION

30 sec • 1 pt

How do you determine the optimal value of 'k'?

Use the elbow method, silhouette score, or cross-validation.

Use the random search method

Select 'k' based on the dataset size

Choose 'k' as the maximum number of clusters

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