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Bias-Variance Tradeoff
The bias-variance tradeoff is the situation where some changes to a learning algorithm reduce bias errors at the cost of increasing variance, while other changes reduce variance at the cost of increasing bias. For example, increasing model size or adding features generally reduces bias but can increase variance, while adding regularization generally increases bias but reduces variance.
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Machine Learning
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Supervised Learning
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Bias (Informal Definition)
Variance (Informal Definition)
Adding More Training Data Does Not Always Help
Total Error Equals Bias Plus Variance for Mean Squared Error
Estimating the Optimal Error Rate
Bias-Variance Tradeoff
Learning Curve for Dev-Set Error
Deciding Whether to Reduce Bias, Variance, or Data Mismatch
High Avoidable Bias with 10% Training, 11% Training-Dev, and 12% Dev Error
Algorithms Can Simultaneously Have Avoidable Bias, Variance, and Data Mismatch Problems
High Variance Bias-Variance Example for Cat Classification
High Bias Low Variance Bias-Variance Example
High Bias and High Variance Bias-Variance Example
Low Bias and Low Variance Bias-Variance Example
According to Machine Learning Yearning, what are the two major sources of error in machine learning?
Understanding bias and variance helps you decide whether adding more training data or other tactics to improve performance are a good use of time.
According to Machine Learning Yearning, the two major sources of error in machine learning are bias and _____.
Which two fundamental error components does Andrew Ng identify as targets for ML optimization?
Understanding bias and variance helps you decide whether adding more training data is a good use of time.
Machine Learning Yearning identifies _____ and variance as the two major sources of error in machine learning.
Match each term to its role in ML Yearning's two-major-sources-of-error framework.
Order the conceptual steps a practitioner follows when applying the bias-variance framework to guide improvement efforts.
What practical benefit does ML Yearning say comes from understanding bias and variance?
Machine Learning Yearning describes bias and variance as the only sources of error in machine learning.
Understanding bias and variance helps you decide whether _____ are a good use of time.
Match each child concept to the aspect of the bias-variance framework it addresses.
Order the reasoning steps a practitioner takes when deciding whether adding training data will improve performance.
Analyzing Error Sources to Direct Machine Learning Development Efforts
Evaluating Team Strategy for Improving an Image Classifier Using Error Analysis
Guiding Development Tactics Through Machine Learning Error Analysis
Learn After
Modern Deep Learning Softens the Bias-Variance Tradeoff
According to Machine Learning Yearning, what is the typical effect of adding regularization to a learning algorithm?
True or False: Adding more neurons or layers to a neural network generally reduces bias but could increase variance.
The bias-variance tradeoff exists because some changes to a learning algorithm reduce bias at the cost of increasing _____, and vice versa.
What is the typical effect of increasing the size of a neural network (adding neurons or layers) on bias and variance?
Adding regularization to a learning algorithm generally reduces bias while also reducing variance.
Some changes to a learning algorithm reduce _____ errors at the cost of increasing variance, while other changes do the reverse.
Match each signal or change to its correct role in the bias-variance tradeoff.
Arrange the steps a practitioner follows when applying the bias-variance tradeoff to decide how to improve a model.
A team's model has high bias (underfits training data). Per the bias-variance tradeoff, which action is most appropriate?
The bias-variance tradeoff implies that most single algorithmic changes will improve both bias and variance at the same time.
According to ML Yearning, adding _____ generally increases bias but reduces variance.
Match each bias-variance tradeoff concept to its correct description from ML Yearning.
Arrange the reasoning steps for deciding whether to increase model size or add regularization to address an error problem.
How do changes in model features and regularization options illustrate the bias-variance tradeoff?
Diagnosing error trade-offs when modifying network size and regularization
What is the fundamental relationship between bias and variance when modifying a learning algorithm?