leave p out cross validation complete code

import numpy as np
import pandas as pd
from numpy import array
from sklearn.model_selection import cross_validate
from sklearn.model_selection import KFold
from sklearn.model_selection import LeavePOut
from sklearn.ensemble import RandomForestClassifier
import warnings
warnings.filterwarnings('ignore')



df=pd.read_csv("https://archive.ics.uci.edu/ml/machine-learning-databases/00611/accelerometer.csv")
y=df.pop("wconfid")



leavepout = LeavePOut(p=2)
model = RandomForestClassifier()
scoring=['balanced_accuracy','precision_macro','recall_macro']
cv_score = cross_validate(model, df[:6], y[:6],scoring=scoring ,cv=leavepout, n_jobs=-1)
cv_score