Simple Text Classification using Random Forest.

import numpy as np
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.ensemble import RandomForestClassifier
from sklearn import datasets

Following represents the corpus of text data used. The sample data I used is meant to classify text about apple and oranges. This could be used for emails for classifying spam.

corpus = [‘The apple is on sale’,’The oranges in on sale’,’The apple and is present’,’The orange and is present’]

Case for Apple as group 1, and case for orange as group 2.

Y = np.array([1,2,1,2])

Creating bag-of-words using CountVectorizer

vectorizer = CountVectorizer(min_df=1)
X = vectorizer.fit_transform(corpus).toarray()

Lastly, classify the text using random forest tree classifier.

clf = RandomForestClassifier()
clf.fit(X, Y)
clf.predict(vectorizer.transform([‘apple is present’]).toarray())

Case for apple and orange.