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A model is likely to be overfitting if it has a low bias O high bias O high variance O low variance

A model is likely to be overfitting if it has a low bias O high bias O high variance O low variance

Database System Concepts
Database System Concepts
7th Edition
ISBN:9780078022159
Author:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan
Publisher:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan
Chapter1: Introduction
Section: Chapter Questions
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If our training data has 4 features and we apply a polynomial transformation using degree 5 to all our features, how many new features will we have? - 20 - 15 - 25 - 4 There are no graphs or diagrams in the image.
Transcribed Image Text:If our training data has 4 features and we apply a polynomial transformation using degree 5 to all our features, how many new features will we have? - 20 - 15 - 25 - 4 There are no graphs or diagrams in the image.
A model is likely to be **overfitting** if it has a _____. - ○ low bias - ○ high bias - ○ high variance - ○ low variance This is a multiple-choice question designed to test understanding of model overfitting in machine learning. Overfitting occurs when a model learns the training data too well, capturing noise and fluctuations that do not generalize to new data. High variance is often associated with overfitting, as the model is sensitive to small changes in the training dataset.
Transcribed Image Text:A model is likely to be **overfitting** if it has a _____. - ○ low bias - ○ high bias - ○ high variance - ○ low variance This is a multiple-choice question designed to test understanding of model overfitting in machine learning. Overfitting occurs when a model learns the training data too well, capturing noise and fluctuations that do not generalize to new data. High variance is often associated with overfitting, as the model is sensitive to small changes in the training dataset.
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