riiid-aied-challenge explores classification methods to predict whether a student will answer a test question correctly. riiid-aied-challenge attemps to solve the Riiid AIEd Challenge that was presented as the Kaggle competition: Riiid! Answer Correctness Prediction.
Navigate to the riiid-aied-challenge directory and setup a new conda environment using the following commands.
conda create -n riiid python=3.8.5 -y
conda activate riiid
conda install ipykernel -y
ipython kernel install --user --name=riiid
Install the dependencies using the following command.
pip install -r requirements.txt
To train and test the classifiers, run the cells of the Jupyter notebooks, using jupyter lab, ensuring the riiid kernel is selected.
Training data should be placed at data/train.csv. The training data CSV used by riiid-aied-challenge can be found here.
The following are loss vs. epoch plots for the classifier's training stages:
Binary Classifier (3 Layers with Batch Normalization)
Binary Classifier (4 Layers with Batch Normalization)
Binary Classifier (3 Layers without Batch Normalization)
LSTM Binary Classifier (3 LSTM layers, 2 FC layers)
- Rishi Masand
- Arjun Arun
PyTorch [Tabular] — Binary Classification by Akshaj Verma
CNN-LSTM PyTorch RIIID by Shivanand M N