Python: Deeper Insights into Machine Learning

Model selection

There are a number of hyper parameters that can be adjusted to improve performance. It is often not a straightforward process, determining the effect of the various parameters, both individually and in combination with each other. Common things to try include getting more training examples, adding or removing features, adding polynomial features, and increasing or decreasing the regularization parameter. Given that we can spend a considerable amount of time collecting more data, or manipulating data in other ways, it is important that the time you spend is likely to result in a productive outcome. One of the most important ways to do this is using a process known as grid search.

Gridsearch

The sklearn.grid_search.GridSearchCV object ...