Tracing Gradient Descent (TracIn) implements a method of tracing training sample influence on the change in loss on a test sample when training deep neural networks. In an idealized notion, TracIn would quantify the change in loss on a test sample while updating the weights of one training sample. However, this idealized notion is impractical, due to its necessity to know the test sample at training time and the fact that gradient descent is usually done on a group of training samples at a time, not on individual samples. To adapt to these limitations, a model's state can be saved using checkpoints, exporting the model's weights at a certain point during training. The influence score is then calculated by the dot product of the loss gradients of training and test samples, weighted by the learning rate, and summed across all checkpoints. However, TracIn can be further adapted to compute self-influence, meaning the influence of a training sample on its own prediction. For this, the training sample gradients are only multiplied by themselves, denoting one training sample's impact on its own prediction. As mentioned previously, actives tend to have higher influence values than inactives, as their low abundance causes high gradients for their own prediction. However, unlike previous data valuation approaches, TracIn will assign higher influence values for false positives than true positives. This is because the influence calculation, regardless of test-or self-influence, always includes a training sample's gradient on its own prediction. Most true positives can be predicted without their own presence in the training set due to their similarity to other true positives, resulting in comparatively low self-influence scores. False positives, on the other hand, have very large gradients on their own prediction, as without their presence in the training set, they will likely be classified as inactive. Minimal Variance Sampling Analysis (MVS-A) is another GBM-based data valuation approach developed to compute importance scores for training samples during training, similar to TracIn. However, TracIn's approach of tracing gradients dependent on their change of weights is not directly applicable to GBM models, as they only have leaf weights. CatBoost's feature importance calculations use these leaf weights. MVS-A, instead, hypothesizes that monitoring changes in the decision tree structure provides better inductive bias for anomaly detection. Therefore, MVS-A adapts the original TracIn approach to use Minimal Variance Sampling (MVS) probabilities to define influence. MVS was originally developed to optimize tree splits during GBM model construction, sampling the most important samples to most accurately represent the entire dataset. This fits well as a notion of importance. Additionally, MVS-A treats each tree of a gradient boosting model as a checkpoint, similar to the checkpoints in TracIn. The scores are conceptually similar to the TracIn self-influence approach, as, similarly to TracIn, the loss function gradient and hessian of each sample are multiplied by itself, quantifying the change on their own prediction. MVS-A's importance scores are interpreted analogously to TracIn.