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Bias and Variance in Machine Learning

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Questions and Answers

What are the two types of errors in machine learning?

Prediction errors and analysis errors

Reduction errors and classification errors

Underfitting and Overfitting

Bias and Variance (correct)

Which term refers to the difference between the model predictions and actual predictions in machine learning?

Underfitting

Bias (correct)

Overfitting

Variance

What is the main aim of ML/data science analysts in relation to bias and variance?

To ignore bias and variance

To introduce bias and variance

To reduce bias and variance (correct)

To increase bias and variance

Which type of error in machine learning can be reduced to improve model accuracy?

<p>Bias</p> Signup and view all the answers

What is the measure of how accurately an algorithm can make predictions for the previously unknown dataset in machine learning?

<p>Errors</p> Signup and view all the answers

Study Notes

Errors in Machine Learning

There are two types of errors in machine learning: bias and variance.

Bias and Variance

Bias refers to the difference between the model predictions and actual predictions in machine learning.
Variance refers to the amount by which the model's predictions vary for given data.

Aim of ML/Data Science Analysts

The main aim of ML/data science analysts is to balance bias and variance to improve model accuracy.

Reducing Error

Variance can be reduced to improve model accuracy.

Model Evaluation

The measure of how accurately an algorithm can make predictions for a previously unknown dataset is known as model generalization or model reliability.

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Description

This quiz explores the concepts of bias and variance in machine learning models, which are sources of prediction errors. It delves into how these errors impact the accuracy of predictions and the trade-off between them in model evaluation.