是否有一种直接的方法可以在 Python 中的一组全新数据上使用经过训练的机器学习 Model

Question

I am noticing similar questions on this topic when I search the Internet; however, most of the answers points to generating random data to explain the approach to a viable solution and do not seem to explain what I am trying to understand in Python, sklearn, LogisticRegression.

I am trying to learn and understand the Machine Learning Model Prediction. I visited Kaggle and downloaded the Titanic data to play and build a Survive prediction model. I was able to build a Logistic Regression to train my model and save it for later.

from sklearn.linear_model import LogisticRegression

X_train, X_test, y_train, y_test = train_test_split(data_train[['Sex', 'Pclass', 'Age','Relatives', 'Fare']], data_train.Survived, test_size=0.33, random_state=0)
# print(X_train.shape)
clf = LogisticRegression(random_state=0).fit(X_train, y_train)

# save the model to disk with JobLib
filename = 'final_model_Joblib.sav'
joblib.dump(clf, filename)

I would like to now use this model on a brand new Tatanic data set, attempting to predict the survival, which do not exist in this new data set.

How would I go about importing my trained model on this new Titanic data set to make the prediction, where X_test and y_test represent my new Titanic data without survival data?

# load the model from disk
loaded_model = joblib.load(filename)
result = loaded_model.score(X_test, y_test)
print(result)

Answer 1

Well, the whole purpose of training a model is to predict on the unseen data, given the features and class distribution of features are the same in your training data or the unseen data. Once you dump a model using joblib or pickle it serializes the model (convert into python byte stream object) and if you load it you will get the same object back. You can use loaded_model.predict(x) according to sklearn docs to find the class prediction on unseen data or the score function to get the accuracy score of your model. for more info, you can check this - https://www.geeksforgeeks.org/saving-a-machine-learning-model/ . Hope this answer your question.