hyperparameter optimization

In the last post, we generated our first Random Forest model with mostly default parameters so that we could get an idea of how important the features are. From that we can further reduce the dimensionality of our data set by throwing out some arbitrary amount of the weakest features. We could continue experimenting with the threshold with which to remove "weak" features, or even go back and experiment with the correlation and PCA thresholds as well to modify how many parameters we end up with... but we'll move forward with what we've got. Now that we've got our [...]