我正在使用 python 和 scikit-learn 进行一些分类。
是否可以重用分类器学习的参数？
例如:
from sklearn.svm import SVC
cl = SVC(...) # create svm classifier with some hyperparameters
cl.fit(X_train, y_train)
params = cl.get_params()
让我们将这个 params 作为字符串字典存储在某处，甚至写入文件 json。假设，我们稍后想使用这个经过训练的分类器对某些数据进行一些预测。尝试恢复它:
params = ... # retrieve these parameters stored somewhere as a dictionary
data = ... # the data, we want make predictions on
cl = SVC(...)
cl.set_params(**params)
predictions = cl.predict(data)
如果我这样做，我会得到 NonFittedError 和以下堆栈跟踪:
File "C:\Users\viacheslav\Python\Python36-32\lib\site-packages\sklearn\svm\base.py", line 548, in predict
y = super(BaseSVC, self).predict(X)
File "C:\Users\viacheslav\Python\Python36-32\lib\site-packages\sklearn\svm\base.py", line 308, in predict
X = self._validate_for_predict(X)
File "C:\Users\viacheslav\Python\Python36-32\lib\site-packages\sklearn\svm\base.py", line 437, in _validate_for_predict
check_is_fitted(self, 'support_')
File "C:\Users\viacheslav\Python\Python36-32\lib\site-packages\sklearn\utils\validation.py", line 768, in check_is_fitted
raise NotFittedError(msg % {'name': type(estimator).__name__})
sklearn.exceptions.NotFittedError: This SVC instance is not fitted yet. Call 'fit' with appropriate arguments before using this method.
是否可以为分类器设置参数并在不拟合的情况下进行预测？我该怎么做？
最佳答案
请阅读 model persistence in SKLearn :
from sklearn.externals import joblib
joblib.dump(clf, 'filename.pkl')
及以后:
clf = joblib.load('filename.pkl')
关于python - 为分类器设置参数并在不拟合的情况下使用它，我们在Stack Overflow上找到一个类似的问题： https://stackoverflow.com/questions/48252006/