Hands-On Machine Learning for Algorithmic Trading

Performance impact of parameter settings

We can use the analogies to evaluate the impact of different parameter settings. The following results stand out (see detailed results in the models folder):

• Negative sampling outperforms the hierarchical softmax, while also training faster
• The Skip-Gram architecture outperforms CBOW given the objective function
• Different min_count settings have a smaller impact, with the midpoint of 50 performing best

Further experiments with the best performing SG model, using negative sampling and a min_count of 50, show the following:

• Smaller context windows than five lower the performance
• A higher negative sampling rate improves performance at the expense of slower training
• Larger vectors improve performance, ...