A Forest of Trees. (Python Language)

Question

Build a Random Forest model.

Task
You will be given a feature matrix X and target array y. Your task is to split the data into training and test sets, build a Random Forest model with the training set, and make predictions for the test set. Give the random forest 5 trees.

You will be given an integer to be used as the random state. Make sure to use it in both the train test split and the Random Forest model.

Input Format
First line: integer (random state to use)
Second line: integer (number of datapoints)
Next n lines: Values of the row in the feature matrix, separated by spaces
Last line: Target values separated by spaces

Output Format
Numpy array of 1's and 0's

Sample Input
1
10
-1.53 -2.86
-4.42 0.71
-1.55 1.04
-0.6 -2.01
-3.43 1.5
1.45 -1.15
-1.6 -1.52
0.79 0.55
1.37 -0.23
1.23 1.72
0 1 1 0 1 0 0 1 0 1

Sample Output
[1 0 0]

import numpy as np

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)

Answer 1

# 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 2

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)
model=RandomForestClassifier(n_estimators=5,random_state=random_state)
model.fit(x_train,y_train)
print(model.predict(x_test))

Answer 3

Are you tried to solve it?

Answer 4

It's python

Answer 5

You can take help from here

https://www.datacamp.com/community/tutorials/random-forests-classifier-JUMP_LINK__&&__python__&&__JUMP_LINK

Answer 6

https://code.sololearn.com/cXjNcVa7rDCx/?ref=app

Please check this, it's have some error, can you plzzz fix it?

Answer 7

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 8

Try to build it by yourself and post your query here if any error or problem occur in that.

Answer 9

هلو

Answer 10

AYESHA NOOR Strange, when I ran it yesterday, it passed all the test cases, but today it does not. I fixed it so that now it passes all test cases again, please let me know if this works for you now 🐨🐨🐨
https://code.sololearn.com/c8a9a42A18A9

Answer 11

a''rore b (kanterd) (1)

Answer 12

Oh

Answer 13

rf = RandomForestClassifier(n_estimators=100
)

rf.fit
(xtrain, ytrain)

print(rf.score
(xtest, ytest))

Answer 14

This is same with the solution above, but more easier and more updated (9/2021)

import numpy as np

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()])

from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import train_test_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))

Answer 15

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)
model=RandomForestClassifier(n_estimators=5,random_state=random_state)
model.fit(x_train,y_train)
print(model.predict(x_test))

Answer 16

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 17

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)
model=RandomForestClassifier(n_estimators=5,random_state=random_state)
model.fit(x_train,y_train)
print(model.predict(x_test))

Answer 18

I don't get it because I get 2,3,4,5 right but in the sample output it says [1 0 0] (what I got) but in the explanation it says that the output is [1 1 0] and then in Test case 1 it says that the answer is [1 1 0] but it's the same question as the sample?

Answer 19

# 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 20

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)
model=RandomForestClassifier(n_estimators=5,random_state=random_state)
model.fit(x_train,y_train)
print(model.predict(x_test))