general information in model training

project_report.md

@@ -88,15 +88,25 @@ General information:
 Included model families:
 - CNN variants (different fusion strategies)
 - XGBoost
-- Isolation Forest
-- OCSVM
-- DeepSVDD
+- Isolation Forest*
+- OCSVM*
+- DeepSVDD*
+
+\* These trainings are unsupervised, which means only low cognitive load samples are used for training. Validation then also considers high low samples.  
+
+
+Supporting utilities in ```model_training/tools```:
+- `scaler.py`: Functions to fit, transform, save and load either MinMaxScaler or StandardScaler, subject-wise and globally - for new subjects, a fallback scaler (using mean of all subjects scaling parameters) is used
+- `performance_split.py`: Provides a function to split a group of subjects based on their performance in the AdaBase experiments, based on the results created in `researchOnSubjectPerformance.ipynb`
+- `mad_outlier_removal.py`: Functions to fit and transform data with MAD outlier removal
+- `evaluation_tools.py`: Especially used for Isolation Forest, Functions for ROC curve as well as confusion matrix
+
+### 4.1 CNNs
+### 4.2 XGBoost
+### 4.3 Isolation Forest
+### 4.4 OCSVM
+### 4.5 DeepSVDD
 
-Supporting utilities:
-- `model_training/tools/scaler.py`
-- `model_training/tools/performance_split.py`
-- `model_training/tools/mad_outlier_removal.py`
-- `model_training/tools/evaluation_tools.py`
 
 ## 5) Real-Time Prediction and Messaging