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Questions and Answers
What are the two types of errors in machine learning?
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?
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?
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?
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?
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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