Параметры модели машинного обученияPython

Программы на Python
Anonymous
Параметры модели машинного обучения

Сообщение Anonymous »


How do I determine which machine learning models are the most effective? In addition to metrics, have you heard about any parameters, how exactly are they applied and where can I find them? For example, from the Naibe Bayes + BoW / Naive Bayes + TF-IDF models? Or from the models Logistic Regression + BoW / Logistic Regression + TF-IDF / Decision Tree + BoW / Decision Tree + TF-IDF?

example with naive bayes + bow

from sklearn.naive_bayes import MultinomialNB from sklearn.metrics import accuracy_score nb_bow = MultinomialNB() nb_bow.fit(X_train_bow, y_train_encoded) y_pred = nb_bow.predict(X_test_bow) print("Accuracy:", accuracy_score(y_test_encoded, y_pred)) example with naive bayes + tf-idf

nb_tfidf = MultinomialNB() nb_tfidf.fit(X_train_tfidf, y_train_encoded) y_pred = nb_tfidf.predict(X_test_tfidf) print("Accuracy:", accuracy_score(y_test_encoded, y_pred)) Can you explain where you can find documentation about model parameters to improve results?


Источник: https://stackoverflow.com/questions/781 ... parameters

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