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High Variance Bias-Variance Example for Cat Classification
In the cat classification example, estimated bias of 1% and variance of 10% indicates high variance. The classifier has very low training error but fails to generalize to the dev set, which is also called overfitting.
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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
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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.
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Learn After
In the cat classification example, if training error is ~1% and dev error is 11%, what is the estimated variance?
A classifier with high variance has very low training error but fails to generalize to the dev set.
When a classifier has very low training error but high dev error, this is also called _____.
Match each metric or concept to its correct description in the cat classification bias-variance example.
Order the steps used in ML Yearning to diagnose high variance in the cat classification example.
Which pattern of training and dev set errors characterizes a high variance classifier in the cat example?
In ML Yearning's cat classification example, a classifier diagnosed with high variance has high training error.
In the cat classification example, variance is estimated as _____ minus training error.
Match each term to its role in diagnosing high variance from the cat classification example.
Order the reasoning steps to conclude the cat classifier is overfitting based on its error metrics.