Nonlinear Training Set Sizes for Cheaper Learning Curves
Plotting learning curves can be computationally expensive because it may require training many models at different data sizes. When that cost matters, using nonlinearly spaced training-set sizes, such as 1,000, 2,000, 4,000, 6,000, and 10,000 examples, can still provide a clear sense of the curve trends while avoiding evenly spaced sizes.
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Nonlinear Training Set Sizes for Cheaper Learning Curves
What is plotted on the y-axis when constructing a learning curve by varying training set size?
When constructing a learning curve, you train a single model on the full dataset and evaluate it at regular checkpoints during training.
To plot a learning curve, you train _____ copies of the algorithm on training sets of different sizes.
Match each learning curve component to its role in the construction process.
Order the steps to construct a learning curve when 1,000 labeled training examples are available.
In the Machine Learning Yearning example with 1,000 training examples, which approach correctly constructs a learning curve?
When constructing a learning curve by training on subsets of 100, 200, and 300 examples, each model copy is evaluated on the same fixed dev set.
When constructing a learning curve, the x-axis represents _____ and the y-axis represents dev-set error.
Match each training subset description to its position or role on a learning curve built from 1,000 total examples.
Order the reasoning steps that explain why separate model copies must be trained for each subset size when constructing a learning curve.
Explain the methodology for constructing and plotting a dev-set error learning curve by varying training dataset size.
Design a dev-set error learning curve experiment for an algorithm with 1,000 available training examples.
How is dev-set error evaluated and plotted for different training-set sizes when constructing a learning curve?