
Understanding AI Modelling
Authored by Sadhna rawat
Computers
10th Grade
Used 3+ times

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10 questions
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1.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
What is AI modelling?
AI modelling refers to the creation of physical robots for automation.
AI modelling is the process of designing user interfaces for applications.
AI modelling is the process of creating algorithms and models that allow machines to learn from data and make predictions.
AI modelling is the study of human behavior in social settings.
2.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
Name two types of AI models.
Supervised learning models, Unsupervised learning models
Generative models
Reinforcement learning models
Deep learning models
3.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
What is the purpose of training an AI model?
To create a user interface for the AI model.
To store data without processing it.
The purpose of training an AI model is to enable it to learn from data and improve its performance on specific tasks.
To generate random outputs without learning.
4.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
Explain the term 'dataset' in AI modelling.
A dataset is a type of AI algorithm used for predictions.
A dataset is a structured collection of data used for training and testing AI models.
A dataset is a single data point used in AI analysis.
A dataset is a random collection of unrelated data points.
5.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
What is overfitting in machine learning?
Overfitting is when a model performs well on training data but poorly on new data due to excessive complexity.
Overfitting occurs when a model is too simple and cannot capture the underlying patterns.
Overfitting happens when a model is trained on too much data, leading to confusion.
Overfitting is when a model performs poorly on both training and new data due to lack of data.
6.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
How does supervised learning differ from unsupervised learning?
Supervised learning uses only numerical data, while unsupervised learning uses categorical data.
Unsupervised learning requires labeled data, while supervised learning works with unlabeled data.
Supervised learning is used for clustering, while unsupervised learning is used for classification.
Supervised learning requires labeled data, while unsupervised learning works with unlabeled data.
7.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
What role does data preprocessing play in AI modelling?
Data preprocessing eliminates the need for data analysis in AI modeling.
Data preprocessing is only necessary for supervised learning models.
Data preprocessing enhances data quality and prepares it for effective AI modeling.
Data preprocessing reduces the amount of data available for AI modeling.
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