Walkthroughs
- Scenario 1 walkthrough
- Hypothetical data set:
- Scenario 1: From scratch…
- Part 1: Create a new project
- Part 2: Load project
- Step 1: Load Project Config
- Step 2 (Optional step) : Import more DLC Tracking Data or videos
- Step 3: Set video parameters
- Step 4: Outlier Correction
- Step 5: Extract Features
- Step 6: Label Behavior (i.e, create annotations for predictive classifiers)
- Step 7: Train Machine Model
- Step 8. Evaluating the model on new (out-of-sample) data.
- Scenario 2 walkthrough
- Scenario 3 walkthrough
- Hypothetical Experiment:
- Scenario 3: Updating a classifier with further annotated data.
- Scenario 3 walkthrough
- Hypothetical Experiment:
- Scenario 4: Analyzing and adding new Experimental data to a previously started project.
- Part 1: ‘Clean up your previous project’ (.. or alternatively create a new project).
- Part 2: Load the project and import your new data.
- Part 3: Process the data for Day 2-3 of the experiment.
- Part 4: Run the predictive classifier on the data for Day 2-3.
- Part 4: Analyze Machine Results
- Part 5: Visualizing machine predictions
- PART 6: Post-classification Validation (detecting false-positives)