Finding the most common words
Its always advisable once you have created your features to inspect them to ensure that they are as you would expect. This will allow you to catch errors early, and perhaps influence what further feature engineering you will need to do.
The vectorizer (cv) you fit in the last exercise and the sparse array consisting of word counts (cv_trigram) is available in your workspace.
Deze oefening maakt deel uit van de cursus
Feature Engineering for Machine Learning in Python
Oefeninstructies
- Create a DataFrame of the features (word counts).
- Add the counts of word occurrences and print the top 5 most occurring words.
Praktische interactieve oefening
Probeer deze oefening eens door deze voorbeeldcode in te vullen.
# Create a DataFrame of the features
cv_tri_df = ____(____,
columns=cv_trigram_vec.get_feature_names()).add_prefix('Counts_')
# Print the top 5 words in the sorted output
print(cv_tri_df.sum().____(ascending=____).head())