Draft for chapters trees, ordered boosting, and imputation🌮 (#81)

Adresses #10

references/obsidian/chapters/environment.md

@@ -34,7 +34,6 @@ dependencies = [
   "wandb==0.13.5",
 ]
 ```
-
 - For presentation of chapter see: [[@prokhorenkovaCatBoostUnbiasedBoosting2018]]
 - source code of experiments and paper is available at https://github.com/KarelZe/thesis/
 - Get some inspiration from https://madewithml.com/#mlops

references/obsidian/chapters/feature_engineering.md

@@ -72,7 +72,6 @@
 - In my dataset the previous or subsequent trade price is already added as feature and thus does not have to be searched recursively.
 - Motivation for scaling features to $[-1,1]$ range or zero mean. https://stats.stackexchange.com/questions/249378/is-scaling-data-0-1-necessary-when-batch-normalization-is-used
 - If needed tokenization support: https://github.com/google/sentencepiece
-
 - Visualize behaviour over time e. g., appearing `ROOT`s and calculate statistics. How many of the clients / percentage are in the train set and how many are just in the test set?
 ![[uuid_over_time.png]]
 (found at https://www.kaggle.com/competitions/ieee-fraud-detection/discussion/111284)

reports/Content/main.tex

@@ -376,10 +376,11 @@ \subsubsection{ISE Data Set (0.5~p)}\label{sec:ise-data-set}
 
 \subsubsection{CBOE Data Set (0.5~p)}\label{sec:cboe-data-set}
 
-\subsubsection{Generation of True
-  Labels (0.5~p)}\label{sec:generation-of-true-labels}
+\subsubsection{Exploratory Data Analysis (2~p)}\label{sec:exploratory-data-analysis}
 
-\subsubsection{Feature Engineering (4~p)}\label{sec:feature-engineering}
+\subsubsection{Data Pre-Processing (1~p)}\label{sec:data-preprocessing}
+
+\subsubsection{Feature Engineering (1.5~p)}\label{sec:feature-engineering}
 
 \subsubsection{Train-Test Split (0.5~p)}\label{sec:train-test-split}