-
Explore the various aspects of the data, visualizing and analyzing it in different ways. It is really important that you are familiar with it. You should describe how you made various design choices, based on the dataset exploration.
(数据可视化，把数据熟悉一下，都做一下)
-
Exploration of at least one or two techniques on which we did not spend significant time in class. For example, using neural networks, support vector machines, or random forests are great ideas; but if you do this, you should explore in some depth the various options available to you for parameterize the model, controlling complexity, etc. (This should involve more than simply varying a parameter and showing a plot of results.)
(每个人选用不同的方法，提到了神经网络，决策树，支持向量机，我们分一下)
-
Other options might include feature design, or optimizing your models to deal with special aspects of the data (missing features, too many features, large numbers of zeros in the data; possible outlier data; etc.). Your report should describe what aspects you chose to focus on.
(不了解)
-
Performance validation. You should practice good form and use validation or cross-validation to assess your models’ performance, do model selection, combine models, etc. You should not simply try a few variations and assume you are done.
(自己测试下效果)
-
Adaptation to under- and over-fitting. Machine learning is not very “one size fits all”; it is impossible to know for sure what model to choose, what features to give it, or how to set the parameters until you see how it does on the data. Therefore, much of machine learning revolves around assessing performance (e.g., is my poor performance due to underfitting, or overfitting?) and deciding how to modify your techniques in response. Your report should describe how, during your process, you decided how to adapt your models and why.
(自己改进下)