Text classification pipeline in ML Python - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to import the necessary module for text vectorization.

ML Python

from sklearn.feature_extraction.text import [1]

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

ATfidfVectorizer

Btrain_test_split

CLabelEncoder

DCountVectorizer

Attempts:

3 left

💡 Hint

Common Mistakes

Choosing CountVectorizer which counts words but doesn't weigh them.

Choosing LabelEncoder which is for labels, not text features.

Choosing train_test_split which is for splitting data.

TfidfVectorizer converts text to numerical features based on term frequency and inverse document frequency, which is common in text classification.

2fill in blank

medium

Complete the code to split the dataset into training and testing sets.

ML Python

from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=[1], random_state=42)

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

A0.2

B0.5

C0.1

D1.0

Attempts:

3 left

💡 Hint

Common Mistakes

Using 1.0 which means all data is test set.

Using 0.5 which splits data evenly, less common for training.

Using 0.1 which is too small for testing.

A test size of 0.2 means 20% of data is used for testing, which is a common practice.

3fill in blank

hard

Fix the error in the model training code by filling the blank with the correct method name.

ML Python

model = LogisticRegression()
model.[1](X_train, y_train)

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Atrain

Bpredict

Cfit_transform

Dfit

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'train' which is not a method in sklearn models.

Using 'fit_transform' which is for transformers, not models.

Using 'predict' which is for prediction, not training.

The 'fit' method trains the model on the training data.

4fill in blank

hard

Fill both blanks to create a dictionary comprehension that maps words to their lengths only if the length is greater than 3.

ML Python

{word: [1] for word in words if len(word) [2] 3}

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

Alen(word)

B<

C>

Dword

Attempts:

3 left

💡 Hint

Common Mistakes

Using '<' instead of '>' which would select shorter words.

Using 'word' as value instead of its length.

Using 'len(word)' in the condition but not as value.

We want the length of each word as the value, and only include words longer than 3 characters.

5fill in blank

hard

Fill all three blanks to create a dictionary comprehension that maps uppercase words to their counts only if the count is greater than 1.

ML Python

{ [1]: [2] for [3] in word_counts if word_counts[[3]] > 1 }

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

Aword.upper()

Bword_counts[word]

Cword

Dword.lower()

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'word.lower()' instead of uppercase.

Using the word itself as value instead of its count.

Using a variable name not in the loop.

We map the uppercase version of each word to its count, iterating over words in word_counts, filtering counts greater than 1.

Practice

1. What is the main purpose of a text classification pipeline in machine learning?

easy

A. To translate text from one language to another

B. To generate new text from existing data

C. To store text data in a database

D. To convert raw text into numbers and classify it

Solution

1. Step 1: Understand the role of a pipeline

   A pipeline processes raw input data step-by-step to prepare it for a model.

2. Step 2: Identify the pipeline's function in text classification

   It converts text into numbers and then classifies the text into categories.

3. Final Answer:

   To convert raw text into numbers and classify it -> Option D

4. Quick Check:

   Text classification pipeline = convert + classify [OK]

Hint: Remember: pipeline = text to numbers + classify [OK]

Common Mistakes:

• Confusing classification with text generation
• Thinking pipeline stores data only
• Mixing translation with classification

2. Which of the following is the correct way to create a simple text classification pipeline using scikit-learn?

easy

A. Pipeline('vect' = CountVectorizer(), 'clf' = LogisticRegression())

B. Pipeline(('vect', CountVectorizer(), 'clf', LogisticRegression()))

C. Pipeline([('vect', CountVectorizer()), ('clf', LogisticRegression())])

D. Pipeline({'vect': CountVectorizer(), 'clf': LogisticRegression()})

Solution

1. Step 1: Recall the Pipeline syntax in scikit-learn

   Pipeline expects a list of tuples, each tuple with a name and a transformer or estimator.

2. Step 2: Check each option's syntax

   Pipeline([('vect', CountVectorizer()), ('clf', LogisticRegression())]) uses a list of tuples correctly. Options A, B, and C use wrong syntax (missing list, wrong brackets, or invalid keyword arguments).

3. Final Answer:

   Pipeline([('vect', CountVectorizer()), ('clf', LogisticRegression())]) -> Option C

4. Quick Check:

   Pipeline needs list of (name, step) tuples [OK]

Hint: Pipeline needs list of (name, step) tuples [OK]

Common Mistakes:

• Using parentheses instead of list brackets
• Passing dictionary instead of list
• Using keyword arguments inside Pipeline

3. Given the following code, what will be the output of print(pipeline.predict(['I love this movie']))?

from sklearn.pipeline import Pipeline
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.linear_model import LogisticRegression

pipeline = Pipeline([
    ('vect', CountVectorizer()),
    ('clf', LogisticRegression())
])

pipeline.fit(['I love this movie', 'I hate this movie'], [1, 0])
print(pipeline.predict(['I love this movie']))

medium

A. [1]

B. [0]

C. [1, 0]

D. Error: model not trained

Solution

1. Step 1: Understand the training data and labels

   The model is trained with two texts: 'I love this movie' labeled 1 (positive), and 'I hate this movie' labeled 0 (negative).

2. Step 2: Predict the label for 'I love this movie'

   Since this exact text was labeled 1 during training, the model should predict 1.

3. Final Answer:

   [1] -> Option A

4. Quick Check:

   Same text as positive label predicts 1 [OK]

Hint: Same input as positive label predicts positive [OK]

Common Mistakes:

• Assuming prediction returns multiple labels
• Forgetting to fit the model before predict
• Confusing label 0 and 1

4. Identify the error in this text classification pipeline code:

from sklearn.pipeline import Pipeline
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.naive_bayes import MultinomialNB

pipeline = Pipeline([
    ('tfidf', TfidfVectorizer),
    ('clf', MultinomialNB())
])

pipeline.fit(['spam message', 'ham message'], [1, 0])

medium

A. MultinomialNB should be replaced with LogisticRegression

B. TfidfVectorizer is not instantiated with parentheses

C. Labels should be strings, not integers

D. Pipeline steps should be a dictionary, not a list

Solution

1. Step 1: Check the pipeline step definitions

   The 'tfidf' step uses TfidfVectorizer without parentheses, so it's a class, not an instance.

2. Step 2: Understand the need for instantiation

   Pipeline steps require instances of transformers/estimators, so TfidfVectorizer() with parentheses is needed.

3. Final Answer:

   TfidfVectorizer is not instantiated with parentheses -> Option B

4. Quick Check:

   Instantiate transformers with () [OK]

Hint: Always add () to instantiate transformers [OK]

Common Mistakes:

• Forgetting parentheses when creating transformer
• Changing model type unnecessarily
• Using wrong data types for labels
• Using dict instead of list for pipeline steps

5. You want to build a text classification pipeline that handles both text cleaning (lowercasing and removing punctuation) and classification. Which approach correctly integrates custom text cleaning into the pipeline?

hard

A. Create a custom transformer class for cleaning, then add it before vectorizer in the pipeline

B. Add text cleaning code inside the LogisticRegression model

C. Clean text after vectorization step in the pipeline

D. Use CountVectorizer with lowercase=True and skip custom cleaning

Solution

1. Step 1: Understand pipeline order and custom steps

   Text cleaning must happen before vectorization to affect input text properly.

2. Step 2: Implement custom cleaning as a transformer

   Creating a custom transformer class allows integration into the pipeline before vectorizer.

3. Step 3: Evaluate other options

   Add text cleaning code inside the LogisticRegression model is wrong because models don't clean text. Clean text after vectorization step in the pipeline is too late to clean text. Use CountVectorizer with lowercase=True and skip custom cleaning only lowercases but does not remove punctuation.

4. Final Answer:

   Create a custom transformer class for cleaning, then add it before vectorizer in the pipeline -> Option A

5. Quick Check:

   Custom cleaning before vectorizer = Create a custom transformer class for cleaning, then add it before vectorizer in the pipeline [OK]

Hint: Clean text before vectorizing using custom transformer [OK]

Common Mistakes:

• Trying to clean text inside the model
• Cleaning text after vectorization
• Relying only on vectorizer lowercase without punctuation removal