I am working with a dataset with about 400 features, all binary (1 or 0). What approach would you recommend? Data set is about 500k records.
Most standard algorithms will work well on binary data, like:
- Decision trees (and random forest)
- Nearest Neighbors
- Neural Networks
But your choice depends on many other things, like:
- What is the expected output?
- Are you doing classification?
- Is it deterministic (same features should always give the same outcome) or stochastic (random factor).
- What is the nature of the database and relationship between the features?
- A 20x20 black-white image.
- A phrase embedded as 20 sequences of 20-size-token.
- A 400 questions true/false exam.
- They can all have the same shape, but are very different in nature and would perform better with different algorithms.
- How disperse / smooth is your data?
- Do all 400 features have the same importance?
- How independent are they?
- How complex the problem really is?
- How much performance do you really need?
- How much work and tuning are you willing to put on this?