Training a Neural Network to Estimate Win Probability
Data Prep Visualization Visualizing the entire dataset used for a training iteration is particularly difficult because it is a very large graph. So instead of visualizing what an actual train/test split looks like, here's an example on a small example graph in figure 1. Figure 1: Example Illustration of Train/Test Split The goal of this figure is to demonstrate that the test dataset is built by randomly selecting about 10% of the nodes from the graph created using MCTS, and then the remaining data is the train dataset. Again, as a reminder, the data contained in each node is the following: Figure 2: Reminder of how our data is stored in nodes An example graph with 10,000+ nodes for reference can be found here , or you can generate your own dataset using the code here . The large number of features in the dataset (upwards of 700) and the number of nodes in even just a small graph (10,000+) make it particularly difficult to visualize the dataset well. Figure 1 and ...