implementation md edit

docs/source/implementation.md

@@ -103,4 +103,4 @@ The final stage uses the processed and cached data to train an XGBoost model. Th
 
 - **Iterator for Data Loading**: Custom iterators are designed to load sparse matrices efficiently into the XGBoost training process, which can handle sparse inputs natively, thus maintaining high computational efficiency.
 - **Training and Validation**: The model is trained using the tabular data, with evaluation steps that include early stopping to prevent overfitting and tuning of hyperparameters based on validation performance.
-- **Hyperaparameter Tuning**: We use [optuna](https://optuna.org/) to tune over XGBoost model pramters, aggregations, window_sizes, and the minimimum code inclusion frequency.
+- **Hyperaparameter Tuning**: We use [optuna](https://optuna.org/) to tune over XGBoost model pramters, aggregations, window sizes, and the minimimum code inclusion frequency.