Library to create simulation to find out what train test ratio is ideal
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train_test_sim
A library to create quick simulation of optimal train-test size you can keep
Developed by Marcel Tino (c) 2024
Examples of How To Use the library
You can use this to alter according to your requirements
##syntax
model=RandomForestClassifier()
get_simulation(X,Y,model)
you can use any model on sklearn or xgboost. All you need to do is specify correct model name
from sklearn.datasets import load_diabetes
import numpy as np
from sklearn.ensemble import RandomForestClassifier
diabetes = load_diabetes()
X, y = diabetes.data, diabetes.target
# Convert the target variable to binary (1 for diabetes, 0 for no diabetes)
Y = (y > np.median(y)).astype(int)
model = RandomForestClassifier()
get_simulation(X, Y, model)
Note: We can create this for any model
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