How to get the same results in different iterations in RandomForest in sklearn

Question

I am using Random Forest classifier for the classification and in each iteration I get different results. My code is as follows.

input_file = 'sample.csv'

df1 = pd.read_csv(input_file)
df2 = pd.read_csv(input_file)
X=df1.drop(['lable'], axis=1)  # Features
y=df2['lable']  # Labels
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3)

clf=RandomForestClassifier(random_state = 42, class_weight="balanced")
clf.fit(X_train,y_train)
y_pred=clf.predict(X_test)
print("Accuracy:",metrics.accuracy_score(y_test, y_pred))

As suggested by other answers I added the parameter n_estimators and random_state. However, it did not work for me.

I have attached the csv file here:

I am happy to provide more details if needed.

Answer 1

You need to set the random state for the train-test splitting as well.

The following code would give you a reproducible results. The recommended approach is not to change the random_state value for improving performance.

import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.ensemble import  RandomForestClassifier
from sklearn import metrics

df1=pd.read_csv('sample.csv')

X=df1.drop(['lable'], axis=1)  # Features
y=df1['lable']  # Labels
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3,random_state=5)

clf=RandomForestClassifier(random_state = 42, class_weight="balanced")
clf.fit(X_train,y_train)
y_pred=clf.predict(X_test)
print("Accuracy:",metrics.accuracy_score(y_test, y_pred))

Output:

Accuracy: 0.6777777777777778