---
# System prepended metadata

title: Questions

---

# Questions

- Start with bad scores on data analysis and error analysis 
    - Briefly discuss old data analysis
    - New data analysis
        - Shoulder to shoulder distance
        - Angles between shoulders 
            - Tried rotation -> no camera -> no work
            - Rotation independent features ?
            - Better way of rotating ? 
    - Missing data analysis ? why only C :'(
- More noise removal ?
    - Current noise removal: shoulder to shoulder distance normalization
    - Binning? Rounding?
- Large gap between train and validation and learning curve still flattens (even with new features)
    - Hypothesis 1: Splits not identically distributed ?
        - Kind of, but also the case for less folds because of grouped split
        - Still some large variations in fold socres
    - Hypothesis 2: Too many features, bad feature selection (SelectKBest) for old feature set (tried PCA as well)
    - Still use a lot of the old features
    - From data analysis we learned that face features least important so we removed these already
    - Still using too many old features from stage 1 ?
    - How to do better feature selection for features from stage 1 ? Highly correlated - better alternatives than SelectKBest
- Current model going in good direction? (double column transformer model)
- How to deal with left and right handed signers -> influence on folds



Solutions:

Dominant hands: Data augmentation, mirror the signs



meer features, 
selectk best behouden?
binning
vingers to noisy
shoulder distance normalizeren is goed
roteren schouders niet overtuigd
ROBUST features
meer dan 5 folds nemen?


## Feedback

- Just augment the data for left-right signers
- Increase resolution in time of new features
- SelectKBest not greatest but is here probably not the problem
- Finger distance probably shaky!
    - Try running averages over time series maybe?
    - Use binning?
- Maybe use denser grids and finetune more? Maybe use more folds
- keep using the old features but try to include all the new features we can get our hands on



## Session 02 december Feedback
- Do no check all classes at same time for robustness (scatter plots). Check classes for whic the feature intended.
    - Mutual Information (but less informative)
- Calculate things from the keypoints, not just statistics. Trajectories / speeds
- Spacial features: Area's or intersection
- Prof. Dambre post is about new features
-  For 2 part splitting: Maybe not try two halves of same length, but different lengths?
