Machine Learning - A Forest of Trees

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]

Answer 1

this is enough to answer all of them

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 2

but the error starts from here
from sklearn.ensemble import

Answer 3

It's my code

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=randomState)

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

print(rf.predict(X_test))

Answer 4

i still find error in other part, i have tried to fix it, but the result is still the same, can you write your full code, so i can understand it better

Answer 5

the result is still wrong, I've replaced it with a string with sign (" ") and (' ')

Answer 6

I've found the problem, and it's worked, I'm very happy to be able to discuss with you, thank you for your time

Answer 7

Problem are not sure

Answer 8

Thank you for the highlight.

Answer 9

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

print(rf.predict(X_test))

Answer 10

Here is my answer:

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()])

# setting the random_state from input()
X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=random_state)

# setting the number of trees we want from input()
rf = RandomForestClassifier(n_estimators=n)
rf.fit(X_train, y_train)

# printing the result
print(rf.predict(X_test))