Prediction/Classification within each grup (Multi-Class / Multi-Label)

Question

I would like to make a prediction of a list of products (>1000 in dataset) within a particular product category (>100 in dataset).

Example:

1. Select product categories (1 or many): fruits, vegetables or more;
2. Model makes predictions and possibly return:

• Fruits: Banana, Apple, Strawberry;
• Vegetables: Carrot, Onion, Potato;

Assumptions:

• Product category/categories to be entered by the user and prediction will happen within this group or groups (it is important to return all of them in the results);

Potential solution:

• Regarding products classification I take into consideration Multi-Class Classification (One vs Rest or One vs One => however, I am afraid that it will required way to much computing power and time)
• The tricky part is to make prediction within a category, does it means that I need to build a separate model for each category?

Answer 1

Below is some sample data limited to few examples only out of >1000. First picture is a colored print-screen of a table to highlight different categories: main, macro-, micro-categories & below the picture is a raw data. The objective is:

1. User enters two (can be one or even all) macro-categories (e.g. Citrus and Leafy green)
2. For these macro-categories (which were entered) the model returns top best matches
3. I am afraid that if I train a model (RF or SVM) over whole dataset, using macr- and micro-categories as different features in the model -> the model could predict micro-category not related to the macro-category (e.g. I will get as a result Oranges (Citrus), but I entered to the model only Berries and Melons).

Main_class	Macro_class	Micro_class	Performance	Usage	Experience
Fruits	Berries	Blueberries	1	0.5	6.05
Fruits	Berries	Raspberries	2	1	12.1
Fruits	Berries	Goji berries	3	1.5	18.15
Fruits	Berries	Strawberries	4	2	24.2
Fruits	Berries	Bilberries	5	2.5	30.25
Fruits	Berries	Açaí berries	6	3	36.3
Fruits	Berries	Cranberries	7	3.5	42.35
Fruits	Berries	Grapes	8	4	48.4
Fruits	Citrus	oranges	21	10.5	127.05
Fruits	Citrus	grapefruits	23	11.5	139.15
Fruits	Citrus	mandarins	25	12.5	151.25
Fruits	Citrus	limes	27	13.5	163.35
Fruits	Stone fruit	nectarines	6	3	36.3
Fruits	Stone fruit	apricots	7	3.5	42.35
Fruits	Stone fruit	peaches	8	4	48.4
Fruits	Stone fruit	plums	9	4.5	54.45
Fruits	Melons	watermelons	2	1	12.1
Fruits	Melons	rockmelons	3	1.5	18.15
Fruits	Melons	honeydew melons	4	2	24.2
Vegetables	Leafy green	lettuce	25	12.5	151.25
Vegetables	Leafy green	spinach	27	13.5	163.35
Vegetables	Leafy green	silverbeet	6	3	36.3
Vegetables	Cruciferous	cabbage	7	3.5	42.35
Vegetables	Cruciferous	cauliflower	8	4	48.4
Vegetables	Cruciferous	Brussels sprouts	25	12.5	151.25
Vegetables	Cruciferous	broccoli	27	13.5	163.35
Vegetables	Marrow	pumpkin	6	3	36.3
Vegetables	Marrow	cucumber	7	3.5	42.35
Vegetables	Marrow	zucchini	8	4	48.4
Vegetables	Allium	onion	5	2.5	30.25
Vegetables	Allium	garlic	6	3	36.3
Vegetables	Allium	shallot	7	3.5	42.35