Learn Before
Constructing a Validation-Error Learning Curve from Training-Set Subsets
Choose several increasing training-set sizes and train a separate copy of the same learning algorithm on a subset of each size. Evaluate every trained model on the same development set, then plot the number of training examples on the x-axis and development-set error on the y-axis. For example, from a pool of 1,000 labeled examples, models could be trained on subsets of 100, 200, 300, and progressively larger sizes through 1,000 examples.
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
Larger Training Sets Usually Lower Dev-Set Error
Target Error Rate on a Learning Curve
Using a Development-Set Curve to Estimate the Payoff of More Data
Using Training Error to Judge the Value of More Data
Why plot training and development error together on a learning curve?
Tiny Training Samples Make Learning Curves Unstable
What are the axes of a learning curve?
A learning curve shows error versus network depth.
Learning Curves and Validation Error
Parts of a learning curve
Constructing a learning curve for model selection
What a learning curve shows
Using a learning curve to judge whether more data will help
What a Learning Curve Shows
What belongs on the vertical axis of a learning curve?
A learning curve is less informative than a single validation score.
Constructing a Validation-Error Learning Curve from Training-Set Subsets
Learn After
Using Uneven Training Sizes to Save Time on Learning Curves
What belongs on the vertical axis of a learning curve built from different training-set sizes?
A learning curve is built by training one model on the full training set and then reading off performance at different checkpoints during that same run.
To make a learning curve, you train _____ versions of the model on training sets of different sizes.
Match each learning-curve element to its job in the experiment.
Arrange the steps for building a learning curve from a labeled training pool of 800 examples.
Which procedure correctly builds a learning curve from a fixed pool of 1,200 labeled examples?
Using the Same Validation Set for Subsampled Training Runs
Learning-curve axes
Match each training subset description to its role on a learning curve built from 1,200 total examples.
Arrange the steps for building a learning curve from multiple training sizes
How to build a dev-set error curve from different training set sizes
Plan a learning-curve experiment with 800 labeled examples.
What metric goes on the y-axis of a learning curve built from different training-set sizes?