Bag of Words and TF-IDF in ML Python - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to create a Bag of Words model using CountVectorizer.

ML Python

from sklearn.feature_extraction.text import CountVectorizer

corpus = ['I love machine learning', 'Machine learning is fun']
vectorizer = CountVectorizer()
X = vectorizer.[1](corpus)
print(X.toarray())

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

Afit_transform

Btransform

Cfit

Dfit_predict

Attempts:

3 left

💡 Hint

Common Mistakes

Using only fit() which does not transform the data.

Using transform() before fitting the vectorizer.

The fit_transform method learns the vocabulary and transforms the text data into a Bag of Words matrix in one step.

2fill in blank

medium

Complete the code to create a TF-IDF matrix from the corpus.

ML Python

from sklearn.feature_extraction.text import TfidfVectorizer

corpus = ['Data science is awesome', 'I love data science']
vectorizer = TfidfVectorizer()
X = vectorizer.[1](corpus)
print(X.toarray())

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Afit

Bfit_predict

Ctransform

Dfit_transform

Attempts:

3 left

💡 Hint

Common Mistakes

Using fit() only, which does not transform the data.

Using transform() without fitting first.

The fit_transform method fits the TF-IDF vectorizer to the corpus and transforms the text into TF-IDF features.

3fill in blank

hard

Fix the error in the code to correctly get feature names from the CountVectorizer.

ML Python

from sklearn.feature_extraction.text import CountVectorizer

corpus = ['AI is the future', 'Future is AI']
vectorizer = CountVectorizer()
X = vectorizer.fit_transform(corpus)
features = vectorizer.[1]()
print(features)

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Afeature_names

Bget_feature_names

Cget_feature_names_out

Dfeatures_names

Attempts:

3 left

💡 Hint

Common Mistakes

Using deprecated method get_feature_names() which may cause warnings.

Trying to access attributes that do not exist.

The method get_feature_names_out() is the correct and current way to get feature names from a fitted CountVectorizer.

4fill in blank

hard

Fill both blanks to create a dictionary of word counts for words longer than 3 characters.

ML Python

words = ['data', 'science', 'is', 'fun']
word_counts = {word: [1] for word in words if len(word) [2] 3}
print(word_counts)

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Alen(word)

B>

C<

Dword

Attempts:

3 left

💡 Hint

Common Mistakes

Using the word itself as the value instead of its length.

Using the wrong comparison operator.

The dictionary comprehension counts the length of each word and includes only words longer than 3 characters.

5fill in blank

hard

Fill all three blanks to create a filtered dictionary of words and their counts where count is greater than 1.

ML Python

word_freq = {'data': 2, 'science': 1, 'fun': 3}
filtered = { [1]: [2] for [3] in word_freq.items() if [2] > 1 }
print(filtered)

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Aword

Bcount

Cfor word, count

Ditem

Attempts:

3 left

💡 Hint

Common Mistakes

Using incorrect variable names that don't match the unpacking.

Not filtering correctly by count.

The dictionary comprehension iterates over word-frequency pairs and keeps only those with count greater than 1.