python之Scikit-学习: "The least populated class in y has only 1 member"

我正在尝试使用 Scikit-learn 进行随机森林回归。使用 Pandas 加载数据后的第一步是将数据拆分为测试集和训练集。但是，我收到错误:

The least populated class in y has only 1 member

我在 Google 上进行了搜索，发现了该错误的各种实例，但我似乎仍然无法理解该错误的含义。

training_file = "training_data.txt" 
data = pd.read_csv(training_file, sep='\t') 
 
y = data.Result 
X = data.drop('Result', axis=1) 
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=123, stratify=y) 
 
pipeline = make_pipeline(preprocessing.StandardScaler(), RandomForestRegressor(n_estimators=100)) 
 
hyperparameters = { 'randomforestregressor__max_features' : ['auto', 'sqrt', 'log2'], 
                'randomforestregressor__max_depth' : [None, 5, 3, 1] } 
 
model = GridSearchCV(pipeline, hyperparameters, cv=10) 
 
model.fit(X_train, y_train) 
 
prediction = model.predict(X_test) 
 
joblib.dump(model, 'ms5000.pkl') 

train_test_split 方法生成此堆栈跟踪:

Traceback (most recent call last): 
    File "/Users/justin.shapiro/Desktop/IPML_Model/model_definition.py", line 18, in <module> 
    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.22, random_state=123, stratify=y) 
  File "/Library/Python/2.7/site-packages/sklearn/model_selection/_split.py", line 1700, in train_test_split 
train, test = next(cv.split(X=arrays[0], y=stratify)) 
  File "/Library/Python/2.7/site-packages/sklearn/model_selection/_split.py", line 953, in split 
for train, test in self._iter_indices(X, y, groups): 
  File "/Library/Python/2.7/site-packages/sklearn/model_selection/_split.py", line 1259, in _iter_indices 
raise ValueError("The least populated class in y has only 1" 
ValueError: The least populated class in y has only 1 member, which is too few. The minimum number of groups for any class cannot be less than 2. 

这是我的数据集的示例:

var1    var2    var3    var4    var5    var6    var7    var8    Result 
high    5000.0  0       60      1000    75      0.23    0.75    17912.0 
mid     5000.0  0       60      1000    50      0.23    0.75    18707.0 
low     5000.0  0       60      1000    25      0.23    0.75    17912.0 
high    5000.0  5       60      1000    75      0.23    0.75    18577.0 
mid     5000.0  5       60      1000    50      0.23    0.75    19407.0 
low     5000.0  5       60      1000    25      0.23    0.75    18577.0 

这是什么错误，我该如何摆脱它？

请您参考如下方法:

此行出现错误:

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.22, random_state=123, stratify=y) 

尝试删除stratify=y