Python Machine Learning with NLP: Clustering Quiz

What is the goal of text clustering?

• To label new data based on feature similarity

• To find and analyze the groups that have formed naturally

• To remove stop words and punctuation

• To define groups before looking at data

What is the k-Means clustering algorithm used for?

• To assign each data point to one of K groups based on the features provided

• To produce a 2-dimensional scatterplot of the cosine distance of each article

• To remove stop words and punctuation

• To calculate tf-idf for each term in a document

What does the k-Means algorithm iterate between?

• Feature extraction and data preprocessing

• Normalization and vectorization

• Data assignment and centroid update

• Tokenization and stemming

What is the purpose of removing stop words in data pre-processing?

• To quantify words in a set of documents

• To change all uppercase words into lower case

• To filter out commonly used words which do not convey significant meaning

• To split sentences into words and collect their counts

What is the tf-idf weight used for in text mining?

• To evaluate how important a word/term is to a document in a collection of corpus

• To measure how frequently a term occurs in a document

• To find and analyze the groups that have formed naturally

• To weigh down the frequent terms of less importance while scaling up the rare ones

What is the purpose of vectorization in data preparation?

• To split sentences into words and collect their counts

• To change all uppercase words into lower case

• To remove unnecessary punctuations from the text

• To turn text into numerical vectors for clustering algorithms

What is the first step in representing an article as a vector?

• Tokenization with NLTK

• tf-idf with Scikit-learn

• Stop Word Removal

• Stemming with Porter Stemmer

What does the TfidfVectorizer do in the tf-idf process?

• Turns text into numerical vectors for clustering algorithms

• Splits sentences into words and collects their counts

• Removes stop words and punctuation from the text

• Creates a dictionary of 'known' words based on the input text and calculates the tf-idf for each term found in an article

What is the purpose of the fit_transform function in tf-idf with Scikit-learn?

• Splits sentences into words and collects their counts

• Creates a dictionary of 'known' words based on the input text and calculates the tf-idf for each term found in an article

• Turns text into numerical vectors for clustering algorithms

• Removes stop words and punctuation from the text

What is the purpose of the tf-idf weight in text mining?

• To evaluate how important a word/term is to a document in a collection of corpus

• To measure how frequently a term occurs in a document

• To weigh down the frequent terms of less importance while scaling up the rare ones

• To find and analyze the groups that have formed naturally