I used random forest to select the features but the first test case failed. The test was passed when I switched to linear regression. Can someone explain why this is the case? Usually this type of problem random forest performs far better (I guest it has something to do with the data that is manually made to maintain the linearity between the X and Y)

I used random forest to select the features but the first test case failed. The test was passed when I switched to linear regression. Can someone explain why this is the case? Usually this type of problem random forest performs far better (I guest it has something to do with the data that is manually made to maintain the linearity between the X and Y)

Enter your code here. Read input from STDIN. Print output to STDOUT

import numpy as np

def covariance(x,y): pmax=-1e9 pmax_index=-1 for i in range(5): if (np.cov(x[i],y)[1][0]>pmax): pmax=np.cov(x[i],y)[1][0] pmax_index=i+1 return pmax_index

t = int(input()) for i in range(t): n = input() cgpa = [float(a) for a in input().split()] coeflist = [] marks_1 = [float(x) for x in input().split()] marks_2 = [float(x) for x in input().split()] marks_3 = [float(x) for x in input().split()] marks_4 = [float(x) for x in input().split()] marks_5 = [float(x) for x in input().split()] marks=np.array([marks_1,marks_2,marks_3,marks_4,marks_5]) print(covariance(marks,np.array(cgpa)))

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What is first line input? In given test case it is 1