Application of machine learning

Application of machine learning

I've seen some machine learning questions on here so I figured I would post a related question: Suppose I have a randomly generated food list which includes an entree, dessert, and a drink. An example would be Chicken, cheesecake, orange juice. The user would rank how they personally like this combination from a scale of 1-5. After reach rating, another random set of food would appear.

My question is, what machine learning technique/algorithm would I use to predict what the user would rank a randomly generated set from all their previous data? In essence, use their opinion to predict their rating of new food sets. Any sites/books that may help?

Answer by Betterdev for Application of machine learning

I would look into operation research because usually it's a minimize or maximize problem.

Answer by Ahmed Tanvir for Application of machine learning

As you have a classification column which is the priority column, you can try for decision tree.

Answer by stackoverflowuser2010 for Application of machine learning

You have stated a regression problem because you are trying to predict a continuous numerical value.

For each data instance, you can extract features and associate a value (1-5). The features can be the existence of a dish during the meal (e.g. has_cheesecake, has_orange_juice), where each feature is a boolean. Suppose there are N possible dishes; then each meal is a data instance (also known as a feature vector) with N features and an associated value. Below is an example with N=12, where the last (13th) column is the value.

0 0 0 1 1 0 0 0 1 0 0 0 5  1 0 0 1 0 1 0 0 0 0 0 0 3  0 0 0 0 0 0 1 1 0 0 0 1 4  

You can then feed this into a machine learning program like Weka, and it will create a regression model for you. Then when you want to predict the user's ranking for a new meal, you feed in a new vector where the last column is unknown, like the following:

0 0 1 0 0 0 1 0 0 0 0 1 ?  

The software will return a value to you, like 3.9.

Answer by Ben Allison for Application of machine learning

What you're asking is basically sentiment detection, which has become very popular for doing things like predicting a user's attitude towards a product. Here's a seminal paper, depending on how academically inclined you are.

You could look at this as a regression problem, but a lot of the time people ignore the fact that there are ordinal relations between the classes. If you have no more information on the items in the meal than their names, I'm not sure I'd expect you to do very well at it. You should look for a feature-representation of the courses if at all possible, to improve your ability to predict values.

Answer by bogatron for Application of machine learning

To pick the best classifier, there are several additional characteristics of your problem you should estimate, such as the approximate number of attribute values (i.e., how many entrees, desserts, and drinks are there from which to choose) and approximately how many training examples would you want to provide prior to making predictions. Not all classifiers are good at dealing with sparse data.

If you have a large number of training examples (relative to the number of attribute values), a decision tree classifier is a good place to start. One of the benefits of a decision tree is that the structure of the learned tree is intuitive and provides a simple interpretation of what are the important attributes (and combinations of attributes).

Answer by Hoap Humanoid for Application of machine learning

There are many algorithms that can fit in your problem, some of them can be decision trees, neural nets or support vector machines.

However, when you are working with user opinions, maybe in some cases you don't get the user opinion in all your products (they can skip). I don't know if this is your case, maybe you force them to rank all the products. However, if you give the user the option to skip, you will end up with some of your products that haven't been labelled. In that situation you could use collaborative filtering. This method predicts the estimated selection of a new user even in the situation I explained.

You can find a good tutorial in Ng's course.

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