Python 如何用交叉评分法解决连续性错误？
我对机器学习非常陌生，在过去的两天里，我一直在尝试摆脱Python 如何用交叉评分法解决连续性错误？,python,machine-learning,scikit-learn,random-forest,Python,Machine Learning,Scikit Learn,Random Forest,我对机器学习非常陌生，在过去的两天里，我一直在尝试摆脱未知标签类型：“continuous”错误 我的代码：将numpy作为np导入 import pandas as pd from sklearn.model_selection import train_test_split from sklearn.preprocessing import StandardScaler from sklearn.ensemble import RandomForestClassifier fro
未知标签类型：“continuous”import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import cross_val_score
dataset = pd.read_csv(r'allData.csv', sep=',')
X = dataset.iloc[:, 1:3].values
y = dataset.iloc[:, 4].values
train_features, test_features, train_lables, test_lables = train_test_split(X, y, test_size=10, random_state=10)
feature_scaler = StandardScaler()
train_features = feature_scaler.fit_transform(train_features)
test_features = feature_scaler.transform(test_features)
classifier = RandomForestClassifier(n_estimators=300, random_state=10)
all_accuracies = cross_val_score(estimator=classifier, X=train_features, y=train_lables, cv="warn")
#all_accuracies = cross_val_score(estimator=classifier, X=train_features, y=train_lables, cv=3)
#print(all_accuracies)
cross_val_score未知标签类型：“continuous”如果有帮助的话，我所有的数据都是数字的，有4列300行。您使用的是
RandomForestClassifierRandomForestRegressor