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Bias (Informal Definition)
Informally, an algorithm's bias is the algorithm's error rate on the training set. Roughly, bias is the error rate of the algorithm on the training set when the training set is very large.
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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
Adding Training Data Does Not Help Much When Training Error Is High
Classifier with Low Bias and Low Variance Is Doing Well
Negative Avoidable Bias Indicates Training Set Overfitting
Informal Bias and Variance Definitions Differ from Statisticians' Definitions
Training Set Performance Comes Before Dev/Test Performance
What does an algorithm's bias informally measure according to Machine Learning Yearning?
True or False: In Machine Learning Yearning, an algorithm's informal bias is defined as its error rate on the dev/test set.
Informally, an algorithm's _____ is its error rate on the training set when the training set is very large.
What does 'bias' informally refer to according to Machine Learning Yearning?
Bias is informally defined as the algorithm's error rate on the training set.
Informally, an algorithm's _____ is its error rate on the training set.
Match each term to its description in Machine Learning Yearning's bias/variance framework.
Order the steps to correctly estimate an algorithm's bias according to Machine Learning Yearning.
Why does Machine Learning Yearning qualify bias as the training error rate on a 'very large' training set?
In Machine Learning Yearning, bias is defined as the algorithm's error rate on the dev or test set.
Roughly, the bias is the error rate of your algorithm on your _____ set when you have a very large training set.
Match each aspect of the bias definition to what it represents in Machine Learning Yearning.
Order the reasoning steps to determine whether bias is the primary error source in an underperforming ML algorithm.
Explain the Relationship Between Informal Bias and Training Set Size
Evaluating Training Error as a Measure of Bias
Distinguishing the Informal Definition of Bias