Practical Advice for Applying Machine Learning
Learn how to make decisions, evaluate models, handle bias and variance, and manage real-world ML workflows with cross-validation, error analysis, and transfer learning.
14 min read
3.Advice for applying machine learning
Advice for applying machine learning
Deciding what to try next
Evaluating a model
Model selection and training/cross validation/test sets
Bias and variance
Bias/variance
Regularization and bias/variance
Establishing a baseline level of performance
Learning curves
High bias
High variance
Deciding what to try next revisited
Bias/variance and neural networks
Machine learning development process
Iterative loop of ML development
Error analysi
Adding data
Transfer learning
Full cycle of a machine learning project
Fairness, bias, and ethics
Skewed datasets
Trading off precision and recall
