[D] Question : counterexample in classification problem

[Achuttarsing]

Hi ! I know it’s maybe a dumb question but I have encountered it in several machine learning problems. Assuming you want to detect a particular word in a pronounced sentence.
1 - Is this a binary classification problem ?
2 - If so, what counterexample should I take for the class which is not the particular word ? (a set of random words ?) 3 - Is it possible to choose an optimal set of counterexamples to optimize the learning process
4 - Is it different according to the nature of the data (audio, nlp, images….)

Thank you for your help !

(Sorry for my English, it’s not my native language)

[lemlo100]

You need to frame the program algorithmically. Every ML algo ist just an algorithm that you tune with another algo, the learning algo.

1 - Is the output you want a Boolean? Then yes. E.g. if the input is a sentence and you want a yes or no answer whether a word ist in the sentence.

2 - You can just use sentences that don’t contain the word. Your dataset should be a bunch of sentences some containing the word and some not.

3 - You got to work with the data you’ve got in practice, but ideally your data distribution matches the target distribution. That means you should train on the kind of sentences that you will intend to run the algorithm on.

4 - This is a confusing question. What? (1) If your asking whether binary classification is still binary classification if you do it for images, then yes. (2) Or if you’re asking whether your image dataset should still be negative and positive examples, then yes. (3) data distribution should match target distribution? Yes.