Learn Before
Methods for Reducing High Variance in Machine-Learning Models
Common ways to reduce high variance include collecting more representative training examples, adding regularization, using early stopping, selecting features carefully, and reducing model flexibility. Error analysis can also motivate changes to the input representation or architecture when the model is overly sensitive to its training sample. Reducing model size may lower variance but can increase bias; when computation and model size are not limiting constraints, regularization is often preferable to shrinking the model solely to control variance.
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
Why enlarging a model can lower bias
Why Increasing Model Size and Data Eventually Stops Helping
Methods to Reduce High Avoidable Bias
What should you do when avoidable bias is high?
True or false: When a model has high variance, adding more training examples is often a useful remedy.
For persistent underfitting, increase the _____ of the model.
Match each learning problem or model change with the usual remedy or meaning.
Order the steps for applying a simple model-fix rule.
How the diagnosis determines the fix for bias and variance
Select the right remedy for bias and variance in two projects.
State the recommended fix for each training problem.
Which option applies the rule of thumb correctly to both cases?
Bias and variance call for the same fix
Methods for Reducing High Variance in Machine-Learning Models
Learn After
Stopping Training When Validation Performance Stops Improving
Using a Smaller Model to Control Variance
What is usually the first choice for reducing variance when enough data are available?
Regularization can lower variance even if it increases bias.
Stopping training early based on _____-set error can reduce variance.
Match each variance-reduction tactic with the main consideration that goes with it.
Order the steps for deciding how to reduce overfitting in a model.
Why should shrinking a model be treated as a cautious option when variance is high?
Prefer regularization when model cost is acceptable
How can error analysis suggest new input features?
Which choice describes early stopping for a model with high variance?
Installing a smaller set of features will always reduce variance without changing bias.
Feature Selection and the Variance–Bias Trade-off
Using More Data When Variance Is High
Regularization Lowers Variance but Raises Bias