We have split the data using the train_test_split function from the sklearn library keeping the test_size as 0.2 which means 20 percent of the total dataset will be kept aside for the validation set. Next, we will train the random forest model using the training ...
import streamlit as st from sklearn.datasets import load_boston from sklearn.ensemble import RandomForestRegressor from sklearn.model_selection import train_test_split # Load dataset data = load_boston() X = data.data y = data.target # Split data X_train, X_test, y_train, y_test = tra...