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Algorithms Can Simultaneously Have Avoidable Bias, Variance, and Data Mismatch Problems
An algorithm can suffer from any subset of high avoidable bias, high variance, and data mismatch.
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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
High Avoidable Bias and Data Mismatch Without High Variance
Which statement best describes how avoidable bias, variance, and data mismatch can affect a single learning algorithm?
True or False: A learning algorithm can exhibit high avoidable bias and data mismatch at the same time without necessarily having high variance.
According to Machine Learning Yearning, it is possible for an algorithm to suffer from any _____ of high avoidable bias, high variance, and data mismatch.
Which statement best describes how high avoidable bias, high variance, and data mismatch can co-exist in a single algorithm?
An algorithm can exhibit high variance and data mismatch simultaneously, without suffering from high avoidable bias.
It is possible for an algorithm to suffer from any _____ of high avoidable bias, high variance, and data mismatch.
Match each of the three error sources to the comparison that most directly reveals it.
Order the diagnostic steps for identifying which subset of the three error sources affects an algorithm.
Training error equals human-level error, training-dev error closely matches training error, but dev error is far higher. Which subset of problems is present?
An algorithm must always exhibit all three problems—high avoidable bias, high variance, and data mismatch—together; they cannot occur in isolation.
When training error ≈ human-level and training-dev ≈ training error, but dev error is much higher, the algorithm suffers from data _____ as its primary problem.
Match each two-problem combination to the diagnostic error-gap pattern it produces.
Order the reasoning steps for planning improvements when an algorithm is diagnosed with all three problems simultaneously.
Explaining the Co-existence of Avoidable Bias, Variance, and Data Mismatch
Diagnosing Co-existing Errors in a Speech Recognition System
Subsets of Error Sources in Machine Learning Algorithms