From 2fcb3426bb341f237f46225ec0e20afaddee1db6 Mon Sep 17 00:00:00 2001 From: Joaquin Torres Date: Sun, 7 Jul 2024 13:52:28 +0000 Subject: [PATCH] Update README.md --- README.md | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/README.md b/README.md index 3aece71..8541443 100644 --- a/README.md +++ b/README.md @@ -16,10 +16,10 @@ The dataset has not been provided since the authors do not have permission for i One of the primary challenges we encountered was a significant class imbalance, with a higher number of patients withdrawing from treatment compared to those staying. To address this issue, we implemented four different training approaches or pipelines on both the pre-pandemic and post-pandemic training datasets: -1. **Using the Original Dataset**: The models were trained on the original datasets. -2. **Class Weight Adjustment**: The models were trained on the original datasets but were penalized more heavily for misclassifying the minority class. -3. **Oversampling**: Additional samples were generated for the minority class (patients staying) to balance the dataset. -4. **Undersampling**: Samples from the majority class (patients withdrawing) were reduced to achieve balance. +1. **Using the Original Dataset (ORIG)**: The models were trained on the original datasets. +2. **Class Weight Adjustment (ORIG_CW)**: The models were trained on the original datasets but were penalized more heavily for misclassifying the minority class. +3. **Oversampling (OVER)**: Additional samples were generated for the minority class (patients staying) to balance the dataset. +4. **Undersampling (UNDER)**: Samples from the majority class (patients withdrawing) were reduced to achieve balance. These approaches resulted in multiple training datasets. However, to ensure a fair comparison of the models' performance across different pipelines, we utilized a common test dataset for evaluation, irrespective of the training approach followed. -- 2.24.1