I am a beginner when it comes to machine learning, and I'm having trouble interpreting some of the results I'm getting from my first program. Here's the setup:
I have a dataset of book reviews. These books can be tagged with any number of qualifiers from a set of about 1600. The people reviewing these books can also tag themselves with these qualifiers to indicate that they like to read things with that tag.
The dataset has a column for each qualifier. For every review, if a given qualifier is used to tag both the book and the reviewer a value of 1 is recorded. If there is not a "match" for a given qualifier on a given review, a value of 0 is recorded.
There is also a "Score" column, which holds an integer 1-5 for each review (the "star rating" of that review). My goal is to determine what features are most important to getting a high score.
Here's the code I have right now (https://gist.github.com/souldeux/99f71087c712c48e50b7):
#Determines the importance of individual features within a dataframe
#Grab header for all feature values excluding score & ids
features_list = df.columns.values[4::]
print "Features List: \n", features_list
#set X equal to all feature values, excluding Score & ID fields
X = df.values[:,4::]
#set y equal to all Score values
y = df.values[:,0]
#fit a random forest with near-default paramaters to determine feature importance
print '\nCreating Random Forest Classifier...\n'
forest = RandomForestClassifier(oob_score=True, n_estimators=10000)
print '\nFitting Random Forest Classifier...\n'
feature_importance = forest.feature_importances_
#Make importances relative to maximum importance
print "\nMaximum feature importance is currently: ", feature_importance.max()
feature_importance = 100.0 * (feature_importance / feature_importance.max())
print "\nNormalized feature importance: \n", feature_importance
print "\nNormalized maximum feature importance: \n", feature_importance.max()
print "\nTo do: set fi_threshold == max?"
print "\nTesting: setting fi_threshhold == 1"
#get indicies of all features over fi_threshold
important_idx = np.where(feature_importance > fi_threshold)
print "\nRetrieved important_idx: ", important_idx
#create a list of all feature names above fi_threshold
important_features = features_list[important_idx]
print "\n", important_features.shape, "Important features(>", fi_threshold, "% of max importance:\n", important_features
#get sorted indices of important features
sorted_idx = np.argsort(feature_importance[important_idx])[::-1]
print "\nFeatures sorted by importance (DESC):\n", important_features[sorted_idx]
pos = np.arange(sorted_idx.shape) + .5
X = X[:, important_idx][:, sorted_idx]
return "Feature importance determined"
In short you do not. Decision trees (building block of random forest) do not work this way. If you work with linear models then there is quite simple distinction if feature is "positive" or "negative", because the only impact it can have on the final result is being added (with weight). Nothing more. However, ensemble of decision trees can have arbitrary complex rules for each feature, for example "if book has red cover and have more than 100 pages then if it contains dragons it gets high score" but "if book has blue cover and more than 100 pages then if it contains dragons it gets low score" and so on.
Feature importance only gives you notion which features contributes to the decision, not "which way", because sometimes it will work this, and sometimes the other way.
What you can do? You can add some extreme simplification - assume that you are only interested in feature in complete absence of others, and now - once you know which are important, you can compute how many times this feature is among each class (scores in your case). This way you will get the distribution
P(gets score X|has feature Y)
which will show you more or less if it has (after marginalization) positive or negative impact.