我是机器学习的新手,并试图制作一个项目来让我忙碌,所以我不太了解它是如何sklearn
工作的。主要目标是训练模型来预测分类变量。当我尝试模型labelEncoding
的y
变量时,出现以下错误:
ValueError: not enough values to unpack (expected 3, got 2)
FitFailedWarning)
这是我正在使用的代码
#Rough training
cols_to_use = [col for col in formatData.columns if col not in 'type1']
x = formatData[cols_to_use]
y = formatData.type1
#print(x.columns)
#print(y)
numerical_transformer = SimpleImputer(strategy='constant')
categorical_tansformer = Pipeline(steps=[
('imputer', SimpleImputer(strategy='most_frequent')),
('label', LabelEncoder())
])
preprocessor = ColumnTransformer(transformers=[('num',numerical_transformer),('cat',categorical_tansformer)])
my_pipeline = Pipeline(steps=[('preprocessor',preprocessor),
('model',RandomForestRegressor(n_estimators=50,random_state=0))])
from sklearn.model_selection import cross_validate
from sklearn.model_selection import cross_val_predict
cv_results = cross_validate(my_pipeline,x,y,cv=5,scoring=('r2','neg_mean_absolute_error'))
predictions = cross_val_predict(my_pipeline,x,y,cv=5)
print(cv_results['test_neg_mean_absolute_error'])
print(predictions)
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