Is fitting two RandomForestClassifiers 500 trees each and average their predicted probabilities on the test set more performant than one with 1000?
With a toy dataset, I obtained slightly better results with the RF 1000 trees
import pandas as pd
import numpy as np
from sklearn.datasets import load_breast_cancer
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import train_test_split
from sklearn.metrics import roc_auc_score

from scipy.stats import ks_2samp

import matplotlib.pyplot as plt
plt.style.use(&quot;seaborn-whitegrid&quot;)


n_models = 2
threshold = .5

X, y = load_breast_cancer(return_X_y= True)
X_train, X_test, y_train, y_test = train_test_split(X, y, random_state = 42, test_size = .3)

rForest1000 = RandomForestClassifier(n_estimators= 1000, random_state= 42, oob_score= True).fit(X_train, y_train)
preds1000 = rForest1000.predict_proba(X_test)[:,1]

roc_score = roc_auc_score(y_true = y_test, y_score= preds1000)

print(f&quot;Test set score for 1K trees is : {round(roc_score, 4)}&quot;)

ls = list()
for i in range(n_models):
    model = RandomForestClassifier(n_estimators= 500, random_state= i).fit(X_train, y_train)
    ls.append(model.predict_proba(X_test)[:,1])
preds = np.array(ls).mean(axis =0)

roc_score = roc_auc_score(y_true = y_test, y_score= preds)

print(f&quot;Test set score for 500trees X 2 avg is : {round(roc_score, 4)}&quot;)

fig, ax = plt.subplots(1,2, figsize = (12,5))

ax[0].hist(preds1000[y_test == 0], color = &quot;darkgreen&quot;, alpha = .5)
ax[0].hist(preds1000[y_test == 1], color = &quot;darkred&quot;, alpha = .5)

ax[0].set_title(f&quot;Predictions distribution with 1K Trees RF\n KS: {[round(ks_2samp(preds1000[y_test == 0], preds1000[y_test == 1])[0],3)]}&quot;)

ax[1].hist(preds[y_test == 0], color = &quot;darkgreen&quot;, alpha = .5)
ax[1].hist(preds[y_test == 1], color = &quot;darkred&quot;, alpha = .5)

ax[1].set_title(f&quot;Predictions distribution with X2 500 Trees RF Trees RF\n KS: {[round(ks_2samp(preds[y_test == 0], preds[y_test == 1])[0],3)]}&quot;);