Hands-On Data Structures and Algorithms with Python - Second Edition

Prediction

To test how the trained model works to predict the category of an unknown document, let's consider some example test data to evaluate the model:

    test_data = ["My God is good", "Arm chip set will rival intel"]     test_counts = count_vect.transform(test_data)     new_tfidf = matrix_transformer.transform(test_counts)

The test_data list is passed to the count_vect.transform function to obtain the vectorized form of the test data. To obtain the TF-IDF representation of the test dataset, we call the transform method of the matrix_transformer object. When we pass new test data to the machine learning model, we have to process the data in the same way as we did in preparing the training data.

To predict which category the docs may belong ...