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wrote chapter Isolation forest

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Michael Weig 2026-03-10 19:38:00 +01:00
parent 10fdafa244
commit f344808802
3 changed files with 14 additions and 5 deletions
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2
predict_pipeline/prediction_env.yaml
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@ -193,4 +193,4 @@ dependencies:
- pytz==2025.2
- scikit-learn==1.6.1
- tzdata==2025.3
prefix: C:\Users\micha\anaconda3\envs\310

8
project_report.md
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@ -104,6 +104,14 @@ Supporting utilities in ```model_training/tools```:
### 4.1 CNNs
### 4.2 XGBoost
### 4.3 Isolation Forest
To start with unsupervised learning techniques, `IsolationForest.ipynb`was created to research how well a simple ensemble classificator performs on the created dataset.
The notebook comes with one class grid search for hyperparameter tuning as well as a ROC curve that allows manual fine tuning.
Overall, our experiments have shown, that this approach is not sufficient, with the following results:
| Metric / Model | Isolation Forest |
|----------------|---------|
| Best Balanced Accuracy |0.575|
| Best AUC | 0.617 |
In detail, the classificator tends to classify the majority of samples as low load.
### 4.4 OCSVM
### 4.5 DeepSVDD

9
readme.md
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@ -11,16 +11,17 @@ Activate the conda-repository "".
```bash
conda activate
```
Make sure, another environment that fulfills prediction_env.yaml is available, matching with predict_pipeline/predict.service - See `predict_pipeline/predict_service_timer_documentation.md`
To get an overview over all available conda environments on your device, use this command in anaconda prompt terminal:
**Make sure, another environment that fulfills prediction_env.yaml is available**, matching with predict_pipeline/predict.service
See `predict_pipeline/predict_service_timer_documentation.md`
to get an overview over all available conda environments on your device, use this command in anaconda prompt terminal:
```bash
conda info --envs
```
Create a new environment based on the yaml-file:
Optionally, create a new environment based on the yaml-file:
```bash
conda env create -f prediction_env.yaml
```
Ohm-UX driving simulator jetson board only: The conda-environment `p310_FS_TF` is used for predictions.
### 2) Camera AU + Eye Pipeline (`camera_stream_AU_and_ET_new.py`)