 Kosala algorithme ya apprentissage machine pona tina oyo epesami
Moko ya ba algorithmes ya apprentissage automatique oyo eyebani mingi oyo ekoki kosalelama pona ko classer ba textes na ba catégories ezali 'Naive Bayes Classifier'. En bas ezali ndakisa ya ndenge nini ekoki kosalelama na kosalelaka monoko ya programmation Python: 1. Bandá na kokɔtisa ba bibliothèques oyo esengeli: ```python from sklearn.feature_extraction.text import CountVectorizer from sklearn.naive_bayes import MultinomialNB from sklearn.pipeline import Pipeline from sklearn .datasets import fetch_20newsgroups na sklearn.metrics import classification_report ``` 2. Botia ensemble ya ba données: ```python data = fetch_20newsgroups() X = data.data y = data.target ``` 3. Kabola ensemble ya ba données na formation pe test sets: ```python uta na sklearn.model_selection import train_test_split X_train, X_test, y_train, y_test = train_test_split(X, y, état_aléatoire=42) ``` 4. Kosala classificateur ya Bayes Naïf: ```classificateur ya python = Pipeline([ ('vectorizer', CountVectorizer()), ('classifier', MultinomialNB()) ]) ``` 5. Kopesa formasyo na classifieur na kosalelaka ensemble ya formation: ```python classifier.fit(X_train, y_train) ``` 6 .Meka classifieur na ensemble ya test pona kotala performance na yango: ```python y_pred = classifier.predict(X_test) print(classification_report(y_test, y_pred)) ``` Pipeline esangisaka ba étapes mibale: ya liboso, ebongoli ba données ya texte na kati ya makambo ya motango na kosalelaka 'CountVectorizer' mpe na sima e former classificateur 'MultinomialNB' na ba données numériques oyo ezuami. Processus oyo ekoki kosalelama na mosala nionso ya classification ya texte na ko personnaliser ensemble ya ba données na ba catégories cibles selon cas d’utilisation spécifique.