This is a cross post (see notes on bottom)

Hi,

I need to implement a program that will classify records into 2 categories (true/false) based on some training data, and I was wondering at which algorithm/methodology I should be looking at. There seem to be a lot of them to choose from -- Artificial Neural Network, Genetic Algorithm, Machine Learning, Bayesian Optimization etc. etc., and I wasn't sure where to start. So, my questions is: How should I choose a learning algorithm I should use for my problem?

If this helps, here is the problem I need to solve.

The training data:
The training data consists of many rows like this:

Precursor1, Precursor2, Boolean (true/false)

The run
I will be given a bunch of precursors.
Then,

1. I choose an algorithm A from different algorithms (or dynamically generate an algorithm), and apply it on every possible combinations of these precursors and collect the "record"s that are emitted. The "record" consists of several key-value pairs*.

2. I apply some awesome algorithm and classify these records in to 2 categories (true/false).

3. I will generate a table that has the same format as the train data:
   Precursor1, Precursor2, Boolean

And the whole program is scored based on how many true/false I got right.

*:"Record"s will look like this (hope this makes sense)

Record         [1...*] Score
-Precursor1             -Key
-Precursor2             -Value

There are only a finite number of possible Keys. Records contain different subset of these keys (some records have key1, key2, key3... other records have key3, key4... etc.).

I actually need 2 learning. One is for step 1. I need to have a module that looks at the Precursor pairs etc. and decide what algorithm to apply in order to emit a record for the comparison. Another is for step 2. I need a module that analyzes the collection of records and categorize them into the 2 categories (true/false).

Thank you in advance!

Notes: This is a cross post from math.stackexchange. Somebody suggested that this question is better suited for here. If I could I'd delete the original post but that was unfortunately not possible.

I need to implement a program that will classify records into 2 categories (true/false) based on some training data, and I was wondering at which algorithm/methodology I should be looking at. There seem to be a lot of them to choose from -- Artificial Neural Network, Genetic Algorithm, Machine Learning, Bayesian Optimization etc. etc., and I wasn't sure where to start. So, my questions is: How should I choose a learning algorithm I should use for my problem?

If this helps, here is the problem I need to solve.

The training data:
The training data consists of many rows like this:

Precursor1, Precursor2, Boolean (true/false)

The run
I will be given a bunch of precursors.
Then,

1. I choose an algorithm A from different algorithms (or dynamically generate an algorithm), and apply it on every possible combinations of these precursors and collect the "record"s that are emitted. The "record" consists of several key-value pairs*.

2. I apply some awesome algorithm and classify these records in to 2 categories (true/false).

3. I will generate a table that has the same format as the train data:
   Precursor1, Precursor2, Boolean

And the whole program is scored based on how many true/false I got right.

*:"Record"s will look like this (hope this makes sense)

Record         [1...*] Score
-Precursor1             -Key
-Precursor2             -Value

There are only a finite number of possible Keys. Records contain different subset of these keys (some records have key1, key2, key3... other records have key3, key4... etc.).

I actually need 2 learning. One is for step 1. I need to have a module that looks at the Precursor pairs etc. and decide what algorithm to apply in order to emit a record for the comparison. Another is for step 2. I need a module that analyzes the collection of records and categorize them into the 2 categories (true/false).

Thank you in advance!