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Deciding Whether to Reduce Bias, Variance, or Data Mismatch
Very different techniques should be applied depending on whether a project's current problem is high avoidable bias or high variance. Insights from estimating avoidable and unavoidable bias and variance can be used to prioritize techniques that reduce bias versus techniques that reduce variance.
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References
Machine Learning Yearning (Deeplearning.ai)
Machine Learning Yearning (Deeplearning.ai)
Machine Learning Yearning (Deeplearning.ai)
Machine Learning Yearning (Deeplearning.ai)
Machine Learning Yearning (Deeplearning.ai)
Machine Learning Yearning (Deeplearning.ai)
Machine Learning Yearning (Deeplearning.ai)
Machine Learning Yearning (Deeplearning.ai)
Machine Learning Yearning (Deeplearning.ai)
Tags
Machine Learning
Deep Learning
Supervised Learning
Dive into Deep Learning @ D2L
Data Science
Machine Learning Strategy
Related
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
Methods That Simultaneously Reduce Both Bias and Variance
Simple Bias/Variance Remediation Formula
Modifying Model Architecture Can Affect Both Bias and Variance
What does understanding which component of error is more pressing help you do in an ML project?
The same techniques that reduce bias in a model will also effectively reduce its variance.
Developing good intuition about _____ and Variance will help you choose effective changes for your algorithm.
Match each type of algorithm change or observation to the error component it primarily addresses.
Order the steps in the recommended process for deciding which source of error to address in your ML project.
Which pair of error rates does ML Yearning recommend examining to estimate avoidable bias and variance?
Analyzing which error types your algorithm suffers from most can help you decide whether to focus on reducing data mismatch.
ML Yearning describes using bias/variance analysis to prioritize techniques that reduce bias vs. techniques that reduce _____.
Match each observed error pattern to the remediation focus it suggests.
Order the reasoning steps for deciding whether high bias or high variance is the more pressing problem.
Prioritizing Algorithm Changes Based on Error Components
Prioritizing Error Mitigation in a Specialized Image Recognition Project
Decision-Making Benefits of Analyzing Algorithm Error Types