Open the chapter list

1. Chapter 1. Start Here
2. Chapter 2. Set Up Your Workspace
3. Chapter 3. Prepare Accelerometer Files
4. Chapter 4. Record Observations
5. Chapter 5. Build Your First Model
6. Chapter 6. Predict with a Model
7. Chapter 7. Understand Your Results
8. Chapter 8. Optimise for Deployment
9. Chapter 9. Scripted Reruns
10. Chapter 10. Troubleshooting

Understand Your Results and Export Folder

This is Chapter 7 of 10 in the beginner path.

After the first successful run, many beginners ask the same question: “Which of these files do I actually care about?” That is a good question. A finished moover run contains several outputs, but you do not need to understand every file at once.

In this chapter, we’ll walk through the main files and what they are for.

Start with the run folder

A completed run sits inside runs/<run_id>/. Inside that folder you will usually see several subfolders, including results/, models/, plots/, and qc/.

A useful way to think about them is this:

• results/ holds the data products from the run
• models/ holds the model export bundles
• plots/ holds figures that help you inspect performance
• qc/ holds previews and checks that help you confirm the data looked sensible

The export bundle

Inside the run’s models/ folder, you will find one or more exported model bundles. That bundle is the part most likely to be shared with collaborators.

A typical bundle includes:

• rf_model_full.rds
• feature_manifest.csv
• model_spec.json
• metrics_overall.csv
• metrics_by_class.csv
• confusion_matrix.csv
• test_vectors.csv
• rf_tree_dump.json

Which files matter to different people?

Different users care about different parts of the export.

For the R user

The most important files are usually:

• the model bundle folder itself
• rf_model_full.rds
• feature_manifest.csv
• the metrics files

These are the files you need to inspect the model in R or use it again later.

For a collaborator checking calculations

The most useful files are often:

• feature_manifest.csv
• test_vectors.csv
• test_vectors_all.csv if present
• the metrics files

These help another person confirm that features and predictions are being reproduced correctly.

For someone implementing the model elsewhere

The most useful files are usually:

• feature_manifest.csv
• model_spec.json
• rf_tree_dump.json
• the test vector files

Those files define what needs to be rebuilt outside R and provide concrete examples to test against.

Why test vectors matter

The test vector files are especially useful because they give you real examples of inputs and expected outputs from the model. If somebody is reimplementing the feature calculations in Python or on an embedded device, the test vectors are usually the quickest way to check whether they are getting the same results.

You do not need to memorise everything

A common beginner mistake is to think that understanding moover means understanding every file at once. It doesn’t. Start with the run folder, the export bundle, and the main metrics. The rest becomes easier once those parts feel familiar.

Move through the tutorial
Previous chapter: Chapter 6. Predict Behaviour with an Existing Model
Next chapter: Chapter 8. Optimise a Model for Deployment