# Figure out relative importance of entity attributes

I'm trying to understand how various aspects of a movie contribute to its gross revenue. I want to rank a movie's attributes in that sense - the attributes that most strongly determine the revenue are ranked higher.

Let $$A_1,\ldots,A_n$$ be a list of attributes of a movie and let the possible values of $$A_i$$ be $$a_{i1},a_{i2},\ldots$$. Many of these attributes (like primary genre) are categorical and some of them (like rating) are continuous.

Approach 1: Consider $$A_1$$: I can form groups of movies having the same value of $$A_1$$, e.g. all movies with $$A_1=a_{12}$$ form a group. The other attributes in a group can vary freely. I can then calculate the mean of the revenues of all movies within a group, and then take the variance of means of all groups.

This will give me the "variation in average revenue as we change $$A_1$$ values". If this variation is high, that means changing $$A_1$$ significantly affects the average revenue - so $$A_1$$ should be a highly ranked attribute.

Approach 2: Again consider $$A_1$$: fix the values of all other attributes $$A_2,\ldots,A_n$$ and look at movies with the same values for $$A_2,\ldots,A_n$$ but different values of $$A_1$$. Find the variance in revenues of such movies - call it "$$A_1$$ variance". The attributes $$A_i$$ with highest "$$A_i$$ variance" will be ranked higher.

Approach 3: Train some ML model (not sure which one) with revenue as target variable and attributes as features. Then look at feature importances to get attribute importances.

A few queries:

1. What assumptions do I need to check for approach 1? e.g. minimum size of a group, distribution of other attribute values within a group, etc.
2. Are there any potential flaws or gotchas in approaches 1 and 2?
3. What approach would you prefer out of the three?
4. What ML model should be used for approach 3? I'm confused because there are plenty of regression models: Linear regression, GB regression, random forest regressor, etc.
• 1) Is it clear that the attributes can be consistently ranked? What if A1 is the number of explosions, A2 the number of kisses and A3 a flag indicating RomCom or Action movie? What is the most important attribute then, A1, A2 or A3? 2) Why don't you try all (ok most) possible approaches and pick the best?
– g g
Feb 11 at 22:22