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Questions and Answers
What are the two types of errors in machine learning?
Which term refers to the difference between the model predictions and actual predictions in machine learning?
What is the main aim of ML/data science analysts in relation to bias and variance?
Which type of error in machine learning can be reduced to improve model accuracy?
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What is the measure of how accurately an algorithm can make predictions for the previously unknown dataset in machine learning?
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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.
