A machine learning workflow:
1. Define the problem
2. Assemble a dataset
3. Determine success metrics
4. Decide on evaluation method
5. Prepare the data
6. Establish a baseline
7. Develop a model that beats the baseline
8. Overfit model
9. Regularize model
10. Tune model
1. Define the problem
2. Assemble a dataset
3. Determine success metrics
4. Decide on evaluation method
5. Prepare the data
6. Establish a baseline
7. Develop a model that beats the baseline
8. Overfit model
9. Regularize model
10. Tune model
Where's model validation in this workflow?
Notice that steps 8, 9, and 10 presume the existence of a mechanism to evaluate the model. This means that model validation is implicitly part of this workflow.
Notice that steps 8, 9, and 10 presume the existence of a mechanism to evaluate the model. This means that model validation is implicitly part of this workflow.
"Assembling a dataset" focuses on determining what will be the sources of data that we will need to solve the problem.
Before understanding metrics of success, we need to have access to the data that we will be using.
Later, "Preparing the data" focuses on that data.
Before understanding metrics of success, we need to have access to the data that we will be using.
Later, "Preparing the data" focuses on that data.
As soon as you determine what the dataset looks like, “data analysis” is another implicit step that enables several of the steps described in this workflow.
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