ValueError: Cannot have number of splits n_splits=３ greater than the number of samples:を修正したい！

前提・実現したいこと

エラーを直したい！

発生している問題・エラーメッセージ

ValueError                                Traceback (most recent call last)
<ipython-input-35-962401f675bf> in <module>
     97 x_tes = np.reshape(X_test, (-1,3))
     98 
---> 99 r1.fit(x_tra, Y_train)
    100 predicted_labels_bs = r1.predict(x_tes)
    101 if r1.best_estimator_.score(x_tra,Y_train) < 0.95:

C:\ProgramData\Anaconda3\lib\site-packages\sklearn\model_selection\_search.py in fit(self, X, y, groups, **fit_params)
    720                 return results_container[0]
    721 
--> 722             self._run_search(evaluate_candidates)
    723 
    724         results = results_container[0]

C:\ProgramData\Anaconda3\lib\site-packages\sklearn\model_selection\_search.py in _run_search(self, evaluate_candidates)
   1189     def _run_search(self, evaluate_candidates):
   1190         """Search all candidates in param_grid"""
-> 1191         evaluate_candidates(ParameterGrid(self.param_grid))
   1192 
   1193 

C:\ProgramData\Anaconda3\lib\site-packages\sklearn\model_selection\_search.py in evaluate_candidates(candidate_params)
    709                                for parameters, (train, test)
    710                                in product(candidate_params,
--> 711                                           cv.split(X, y, groups)))
    712 
    713                 all_candidate_params.extend(candidate_params)

C:\ProgramData\Anaconda3\lib\site-packages\sklearn\model_selection\_split.py in split(self, X, y, groups)
    327                 ("Cannot have number of splits n_splits={0} greater"
    328                  " than the number of samples: n_samples={1}.")
--> 329                 .format(self.n_splits, n_samples))
    330 
    331         for train, test in super(_BaseKFold, self).split(X, y, groups):

ValueError: Cannot have number of splits n_splits=2 greater than the number of samples: n_samples=0.

該当のソースコード

%matplotlib inline
import schedule
import time
from time import sleep
from light_progress.commandline import ProgressBar
from sshtunnel import SSHTunnelForwarder
import MySQLdb
import pandas as pd
import numpy as np
from sklearn.svm import SVC
from sklearn.metrics import r2_score
from sklearn.ensemble import RandomForestRegressor
from sklearn.ensemble import ExtraTreesRegressor
import mglearn
from sklearn.model_selection import train_test_split, GridSearchCV
from sklearn import linear_model
import datetime
（略）
first = 5000 
abc = 1
efg = 181 
i = np.array([range(abc,efg,1)])
I = np.array(range(abc,efg,1))
x16 = first-0.3876*I
（略）
x25= np.array([x25])
x26= np.array([x26])
x18= np.array([x18])
x100= np.array(x100)
x13=x25 - x26
XX1=I  
XX = np.array([[x13],[x18],[XX1]])
Y = np.array([x16])
X = XX.T      
X_train, X_test, Y_train, Y_test = train_test_split(X,Y,train_size = 0.7)        
        
search_params = {
    'n_estimators'      : [5, 10, 20, 30, 50, 100, 300, 400, 500,600,1000],
    'max_features'      : [0.1],
    'random_state'      : [2525],
    'n_jobs'            : [1],
    'min_samples_split' : [1,2,3, 5, 10, 15, 20, 25, 30, 40, 50, 100],
    'max_depth'         : [3, 5, 10, 15, 20, 25, 30, 40, 50, 100,1000,1500,10000]
}
 
r1 = GridSearchCV(
    RandomForestRegressor(),
    search_params,
    cv = 3,
    n_jobs = 1,
    verbose=True
)
x_tra = np.reshape(X_train, (-1,3)) 
x_tes = np.reshape(X_test, (-1,3)) 

r1.fit(x_tra, Y_train)
predicted_labels_bs = r1.predict(x_tes)   
(略)