For this classification task, we trained various models including logistic regression, nearest neighbor, decision tree, and multi-layer perceptron using sklearn. For deployment, we used all the data for training while repeating data points of non-rod classes 20 times (again to make non-rod class size similar to rod class size). We trained decision tree with depth 3 and logistic regression (with regularization parameter C=0.5) to obtain interpretable models.