How to choose the best algorithm
Okay, so without going in to details,

Your target values are nominal, discrete values, hence the classification model would solve your problem.

Under classification there are variety of methods available,for example, there is decision tree, Naive Bayes etc.. . you may try all of these and check which gives the better confusion metrics.

Multi-Class means, there are multiple classes , such as in you colors. 

If I have books features as input and i want to predict its genre, and the available categories i can put this book in are- fictional, romance, historical. all these labels makes it multi class because multiple genre.A book can be of any of the genre.

Multi-Label means, an instance can belong to more than 1 class.

Now, if this one book is fictional romance , then this makes it multi label. Because out of 3 available categories, this one book belongs to 2(it can be more than 2 also).

Now, This was just a short answer to get you started,please read on these key words to further details.


  1.) 1 target class - color


This is target attribute, color


  2.) Multiple values for that target class - (red, yellow, blue, green, pink)


Multiple class/labels for target attribute, red, yellow, blue... are classes/labels. Since there are more than 2 classes its multi class.

If you input map to 2 or more colors than 1 then it makes it multi label.