Week 12: Final Week
These past 12 weeks have flown by so fast! It still feels like yesterday I started working on this project. There were highs and lows but at the end of the day we overcame our obstacles to create something to be proud of. It was a great learning experience and I had fun working with the team. Monday: Implemented the prototype of the K decrement algorithm to reduce the number of filter variables in case of shortage of data points upon filtration. Here are some results: K decrement- condP() K = 100 rf = 0.8, minmin = 20 dims, datSize, tries, K = 3 1000 1 100 Average R2: JP, UP, PS = 0.67025996194 0.726423475099 0.7434199290171855 K=100 , rf = 0.8, minmin = 5 dims, datSize, tries, K = 3 1000 5 100 Average R2: JP, UP, PS = 0.70784384556 0.87296540935 0.764876980843549 K=100 , rf = 0.8, minmin = 4 dims, datSize, tries, K = 4 1000 1 100 Average R2: JP, UP, PS = 0.70162883565 0.80123074862 0.6089856810665888 K=100 in condP() function means that w
