I'm applying nearest neighbors algorithm KNN classifier on a movie rating dataset, it was a sparse document-term matrix in the Matrix-Market format.
I've converted it into a format same as this IRIS dataset.
There are 5000 features and 2000 instances. I'm using this code for building this classifier.
The Euclidian Distance measure gives poor results like 6% accuracy, I then tried Cosine Similarity function. The code is as following:
def dot_product(a, b): return sum(map(lambda x: x * x, zip(a, b))) def cosineSimilarity(a, b): sumxx, sumxy, sumyy = 0, 0, 0 for i in range(len(a)): x = a[i]; y = b[i] sumxx += x*x sumyy += y*y sumxy += x*y return sumxy/math.sqrt(sumxx*sumyy)
Even this did not improve results. Most of them are Zeros since not everyone is rating all movies. So I read it somewhere that chaging all zeros to 0.1 will improve results, but that did not improve results as well.
I've tried K-Fold cross-validation too, but as you guessed, it did not improve anything.
My general setting is: k=3, split for Training/testing = 0.9
Are there any obvious improvements? Any mistakes in my code? For now, I am trying only unweighted voting, but suggestions for weighted majority voting are welcome as well.
Note: Using ML libraries is not an option.