## Flowchart: CatBoost Model Training Pipeline ### Overview The image depicts a sequential training pipeline for four CatBoost models, each incorporating progressively more input features. The flow moves left-to-right, with each stage adding new variables to the training process. ### Components/Axes - **Input Variables**: - `X_train` (constant across all stages) - `Y_avail.` (constant across all stages) - `Y_char.` (added in Stage 2) - `Y_pass.` (added in Stage 3) - `Y_other.` (added in Stage 4) - **Output Models**: - `CatBoost 1` (Stage 1) - `CatBoost 2` (Stage 2) - `CatBoost 3` (Stage 3) - `CatBoost 4` (Stage 4) - **Flow Direction**: Left-to-right progression, with each CatBoost model trained on the cumulative set of features from prior stages. ### Detailed Analysis 1. **Stage 1**: - Inputs: `X_train`, `Y_avail.` - Output: `CatBoost 1` - Training arrow connects inputs to model. 2. **Stage 2**: - Inputs: `X_train`, `Y_avail.`, `Y_char.` - Output: `CatBoost 2` - New feature `Y_char.` added to training data. 3. **Stage 3**: - Inputs: `X_train`, `Y_avail.`, `Y_char.`, `Y_pass.` - Output: `CatBoost 3` - Additional feature `Y_pass.` included. 4. **Stage 4**: - Inputs: `X_train`, `Y_avail.`, `Y_char.`, `Y_pass.`, `Y_other.` - Output: `CatBoost 4` - Final feature `Y_other.` incorporated. ### Key Observations - **Feature Accumulation**: Each subsequent CatBoost model includes all previous features plus one new variable (`Y_char.`, `Y_pass.`, `Y_other.`). - **No Numerical Data**: The diagram lacks quantitative values, focusing instead on architectural progression. - **Sequential Dependency**: Later models explicitly depend on earlier training outputs, suggesting iterative refinement. ### Interpretation This pipeline illustrates a **feature engineering strategy** where each CatBoost iteration builds on prior work by incorporating additional variables. The incremental addition of features (`Y_char.`, `Y_pass.`, `Y_other.`) implies an attempt to: 1. Capture more complex patterns in the data 2. Improve model performance through expanded feature sets 3. Test the impact of specific variables on predictive power The absence of a legend or numerical metrics suggests this is a conceptual diagram rather than an empirical analysis. The left-to-right flow emphasizes **cumulative knowledge transfer** between models, with each CatBoost acting as both a consumer and producer in the pipeline. This pattern aligns with ensemble learning principles, where sequential model training can reduce bias and variance.