Explain the methodology for constructing and plotting a dev-set error learning curve by varying training dataset size.
Question: Describe the systematic steps required to construct a learning curve that tracks dev-set error as training set size varies. In your response, explain how the models are trained on different sample sizes and how the final visualization is plotted.
Sample answer: To construct the learning curve, you run the algorithm using different training-set sizes. This is done by training separate copies of the algorithm on subsets of varying sizes (for example, if you have 1,000 total examples, you might train copies on 100, 200, 300, and more examples up to 1,000). Once each copy is trained, you evaluate its performance to determine the dev-set error. Finally, you plot the dev-set error on the y-axis against the corresponding training-set sizes on the x-axis to visualize how the error varies with the amount of training data.
Key points:
- Train separate copies of the algorithm on different training-set sizes.
- Measure the dev-set error for each trained copy.
- Plot dev-set error against the training-set size.
Rubric: The answer must specify: 1) training separate copies of the algorithm on different training-set sizes, 2) evaluating these copies to measure the dev-set error, and 3) plotting dev-set error on the y-axis against training-set size on the x-axis.
0
1
Tags
Machine Learning
Deep Learning
Supervised Learning
Dive into Deep Learning @ D2L
Data Science
Machine Learning Strategy
Machine Learning Yearning @ DeepLearning.AI
Related
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?