Combining Learners & Unsupervised Learning

Combining Learners & Unsupervised Learning

University

20 Qs

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Combining Learners & Unsupervised Learning

Combining Learners & Unsupervised Learning

Assessment

Quiz

Computers

University

Easy

Created by

Ms Viancy V

Used 2+ times

FREE Resource

20 questions

Show all answers

1.

MULTIPLE CHOICE QUESTION

1 min • 1 pt

What are some model combination schemes used in machine learning?

Boosting

Logistic Regression

Bagging, Boosting, Stacking, Ensembling

2.

MULTIPLE CHOICE QUESTION

1 min • 1 pt

Explain the concept of ensemble learning using a real-world scenario. Aditi, Advait, and Shreya are participating in a data science competition where they need to predict the winner of a sports event. They decide to combine their individual models to improve their predictions. Provide examples of bagging, boosting, and stacking.

Boosting aims to decrease model performance

Bagging involves combining multiple models

Ensemble learning combines multiple models to improve performance. Bagging, boosting, and stacking are examples of ensemble learning techniques.

3.

MULTIPLE CHOICE QUESTION

1 min • 1 pt

How does K-means clustering work in a real-world scenario?

In a retail store, K-means clustering works by categorizing customers into different segments based on their purchase history and preferences.

At a music festival, K-means clustering involves grouping attendees into clusters based on their music genre preferences.

For a marketing campaign, K-means clustering is used to segment customers into target groups based on their buying behavior. It works by iteratively assigning customers to the nearest cluster centroid and then recalculating the centroids based on the mean of the assigned customers' characteristics.

4.

MULTIPLE CHOICE QUESTION

1 min • 1 pt

Describe the Instance Based Learning algorithm K-Nearest Neighbors (KNN) using a real-world scenario.

K-Nearest Neighbors (KNN) is an instance-based learning algorithm where the prediction of a new student's favorite ice cream flavor is based on majority voting of its K nearest neighbors.

KNN is a supervised learning algorithm where the prediction of a new student's favorite ice cream flavor is based on majority voting of its K nearest neighbors.

KNN predicts based on a decision tree model where the prediction of a new student's favorite ice cream flavor is based on majority voting of its K nearest neighbors.

5.

MULTIPLE CHOICE QUESTION

1 min • 1 pt

How are Gaussian Mixture Models (GMM) used in real-world scenarios?

GMM is used in real-world scenarios for predicting customer preferences by analyzing their purchase history

GMM is used in real-world scenarios for detecting fraudulent activities by clustering abnormal behavior patterns

GMM is used in real-world scenarios for personalizing recommendations by grouping similar user behaviors

6.

MULTIPLE CHOICE QUESTION

1 min • 1 pt

What is the Expectation Maximization (EM) algorithm and where is it commonly applied?

The EM algorithm is commonly applied in financial forecasting models.

The EM algorithm is used in robotics for path planning.

The EM algorithm is used to estimate parameters of statistical models with latent variables. It is commonly applied in machine learning for clustering algorithms like Gaussian Mixture Models (GMM) and in natural language processing for part-of-speech tagging.

7.

MULTIPLE CHOICE QUESTION

1 min • 1 pt

Explain the concept of voting in machine learning using a real-world scenario.

Voting in machine learning involves randomly selecting a model to make predictions.

Voting in machine learning is the process of selecting the best model based on the highest accuracy.

Voting in machine learning involves combining predictions from multiple models to make a final prediction.

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