Decide First Whether Learning Curves Need Smoothing
Methods such as averaging results from several random subsets or building more balanced subsets are optional tools. Use them only after you have already plotted the learning curves and found that the curves are so irregular that the overall pattern is hard to see. If the training set is very large, such as tens of thousands of examples, and the class proportions are already fairly even, these extra steps are usually unnecessary.
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Decide First Whether Learning Curves Need Smoothing
What should you do separately for each randomly drawn subset when estimating a learning curve?
Averaging Repeated Runs to Smooth a Curve
After training separate models on many random samples of the same size, you plot the _____ training error and validation error across runs.
What is the main benefit of averaging dev-set learning curves from many randomly chosen training subsets?
The averaging method creates several smaller training subsets by sampling with replacement from the original data.
How Many Models Should Be Trained?
Match each term in the resampling method to its correct description.
Order the steps for averaging learning curves over multiple random mini-samples.
When estimating a learning curve by averaging over random subsets, how many subsets are usually sampled?
True or False: In the averaging method, one model is fit to the union of all randomly sampled training subsets, and that single model’s error is then averaged.
Errors recorded before averaging learning curves
Match each problem with the part of the averaging method that addresses it.
Arrange the steps for using repeated subset experiments to clarify a noisy learning curve.
Why do repeated random subsamples make learning curves less noisy, and how is the procedure carried out?
Estimating Curve Values from Repeated Resamples
Resampling Rule and Curves for Reducing Variability
Learn After
When is it worth considering methods that make learning curves easier to interpret?
It is best to smooth the training and validation curves before looking at them for the first time.
Use noise-reduction methods only when the learning curve is too _____ to show the underlying pattern clearly.
Decide When Noise-Reduction Methods Are Worth Considering
Order the steps for deciding whether to smooth noisy learning curves.
When to Use Noise-Smoothing Methods for Learning Curves
Decide whether additional noise-reducing steps are needed for a learning curve.
Deciding Whether to Average Learning Curves
Which situation least suggests that learning-curve noise reduction is needed?
A very large, fairly balanced training set usually makes these noise-reduction tricks unnecessary.