A Forest of Trees Run

Question

I was solving sololearn machine learning random forest project. But I am stuck and I cannot figure out the reason my code is failing. I have passe both the test cases, but I cannot access the hidden test cases.

Here is the code I tried...

import numpy as np
import pandas as pd
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import train_test_split

random_state = int(input())
n = int(input())
rows = []
for i in range(n):
rows.append([float(a) for a in input().split()])

X = np.array(rows)
y = np.array([int(a) for a in input().split()])

X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=n)
rf = RandomForestClassifier(n_estimators=5, random_state=n)
rf.fit(X_train, y_train)
result = rf.predict(X_test)
print(result)

Answer 1

in your model you use random_state=n, while random_state were supposed to be random_state

Answer 2

ANS :

import numpy as np
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import train_test_split

random_state = int(input())
n = int(input())
rows = []
for i in range(n):
rows.append([float(a) for a in input().split()])

X = np.array(rows)
y = np.array([int(a) for a in input().split()])

x_train, x_test, y_train, y_test = train_test_split(X,y,random_state=random_state)

#print(x_train,x_test,y_train,y_test,sep='\n \n')
model = RandomForestClassifier(n_estimators=5,random_state=random_state)

model.fit(x_train,y_train)

pred =model.predict(x_test)
print(pred)

Answer 3

# Author: Abdullah Abdelhakeem Amer#
# Date : 1/3/2021 #
# version : v01 #

import numpy as np
import pandas as pd
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import train_test_split

randomState = int(input())
n = int(input())
rows = []
for i in range(n):
rows.append([float(a) for a in input().split()])

X = np.array(rows)
y = np.array([int(a) for a in input().split()])

X_train, X_test, y_train, y_test = train_test_split(X,y,random_state=randomState)

rf = RandomForestClassifier(n_estimators=5,random_state=randomState)
rf.fit(X_train,y_train)

print(rf.predict(X_test))

Answer 4

USE THIS CODE TO GET ALL TEST CASES RIGHT:
SOLUTION!!

import numpy as np
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestClassifier

random_state = int(input())
n = int(input())
rows = []
for i in range(n):
rows.append([float(a) for a in input().split()])

X = np.array(rows)
y = np.array([int(a) for a in input().split()])

X_train,X_test,y_train,y_test = train_test_split(X,y,random_state=random_state)

rf = RandomForestClassifier(n_estimators=5,random_state=random_state)
rf.fit(X_train,y_train)
print(rf.predict(X_test))