Vocabulary size control in NLP - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to limit the vocabulary size when tokenizing text.

NLP

from sklearn.feature_extraction.text import CountVectorizer
vectorizer = CountVectorizer(max_features=[1])
texts = ['I love machine learning', 'AI is the future']
X = vectorizer.fit_transform(texts)

Drag options to blanks, or click blank then click option'

A1000

B0

CNone

D-1

Attempts:

3 left

💡 Hint

Common Mistakes

Using None or negative numbers which do not limit vocabulary size.

Setting max_features to 0 which disables vocabulary.

Setting max_features to 1000 limits the vocabulary to the top 1000 words by frequency.

2fill in blank

medium

Complete the code to remove rare words by setting a minimum document frequency.

NLP

from sklearn.feature_extraction.text import CountVectorizer
vectorizer = CountVectorizer(min_df=[1])
texts = ['apple banana apple', 'banana orange', 'apple orange orange']
X = vectorizer.fit_transform(texts)

Drag options to blanks, or click blank then click option'

A2

B0.5

C1

D0

Attempts:

3 left

💡 Hint

Common Mistakes

Using integer values without understanding they mean absolute counts.

Setting min_df to 0 which keeps all words.

min_df=0.5 means words must appear in at least 50% of documents to be kept.

3fill in blank

hard

Fix the error in the code to correctly limit vocabulary size and remove rare words.

NLP

from sklearn.feature_extraction.text import CountVectorizer
vectorizer = CountVectorizer(max_features=[1], min_df=[2])
texts = ['cat dog cat', 'dog mouse', 'cat mouse mouse']
X = vectorizer.fit_transform(texts)

Drag options to blanks, or click blank then click option'

A2

B0.5

C1

D3

Attempts:

3 left

💡 Hint

Common Mistakes

Setting min_df too high removes all words.

Using invalid types for max_features or min_df.

max_features=2 limits vocabulary to top 2 words; min_df=1 means words must appear in at least 1 document.

4fill in blank

hard

Fill both blanks to create a vocabulary of words appearing in at least 2 documents and limit size to 3.

NLP

from sklearn.feature_extraction.text import CountVectorizer
vectorizer = CountVectorizer(min_df=[1], max_features=[2])
texts = ['sun moon sun', 'moon star', 'sun star star']
X = vectorizer.fit_transform(texts)

Drag options to blanks, or click blank then click option'

A2

B3

C1

D4

Attempts:

3 left

💡 Hint

Common Mistakes

Swapping min_df and max_features values.

Using values that remove all words.

min_df=2 keeps words in at least 2 documents; max_features=3 limits vocabulary size to 3.

5fill in blank

hard

Fill all three blanks to create a vocabulary with max size 4, min document frequency 2, and max document frequency 0.8.

NLP

from sklearn.feature_extraction.text import CountVectorizer
vectorizer = CountVectorizer(max_features=[1], min_df=[2], max_df=[3])
texts = ['apple banana apple', 'banana orange apple', 'apple orange banana', 'banana apple orange']
X = vectorizer.fit_transform(texts)

Drag options to blanks, or click blank then click option'

A4

B2

C0.8

D3

Attempts:

3 left

💡 Hint

Common Mistakes

Using max_df greater than 1 or less than 0.

Setting min_df higher than max_df causes empty vocabulary.

max_features=4 limits vocabulary size; min_df=2 keeps words in at least 2 documents; max_df=0.8 removes very common words appearing in more than 80% of documents.