Text classification pipeline in ML Python - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Model Choice

intermediate

Choosing the best model for short text classification

You have a dataset of short customer reviews labeled as positive or negative. Which model is most suitable to start with for this text classification task?

A A simple Logistic Regression model with TF-IDF features

B A deep Convolutional Neural Network with raw text input

C A K-Means clustering model to group reviews

D A Principal Component Analysis (PCA) model to reduce text dimensions

💡 Hint

Think about models that work well with labeled data and simple features for text.

Logistic Regression with TF-IDF is a strong baseline for text classification, especially for short texts. CNNs require more data and preprocessing. K-Means is unsupervised, and PCA is for dimensionality reduction, not classification.

❓ Predict Output

intermediate

Output of text preprocessing code

What is the output of the following Python code that preprocesses text for classification?

ML Python

import re
text = "Hello World! This is AI-2024."
processed = re.sub(r'[^a-zA-Z ]', '', text).lower().split()
print(processed)

A ['hello', 'world', 'this', 'is', 'ai']

B ['Hello', 'World', 'This', 'is', 'AI']

C ['hello', 'world', 'this', 'is', 'ai2024']

D ['Hello', 'World', 'This', 'is', 'AI2024']

💡 Hint

Look at how non-letter characters are removed and text is lowered.

The regex removes all characters except letters and spaces, so 'AI-2024' becomes 'AI'. Then the text is converted to lowercase and split into words.

❓ Hyperparameter

advanced

Selecting the best hyperparameter for TF-IDF vectorizer

You want to improve your text classification model by tuning the TF-IDF vectorizer. Which hyperparameter controls the maximum number of features (words) to keep?

A min_df

B max_features

C ngram_range

D stop_words

💡 Hint

This parameter limits the vocabulary size.

max_features sets the maximum number of words to keep based on term frequency. min_df controls minimum document frequency, ngram_range controls word group sizes, and stop_words removes common words.

❓ Metrics

advanced

Interpreting classification report metrics

Your text classification model outputs the following metrics: precision=0.8, recall=0.5, accuracy=0.75. What does the low recall indicate?

A The model has perfect predictions

B The model predicts too many false positives

C The model has balanced errors between classes

D The model misses many positive examples (false negatives are high)

💡 Hint

Recall measures how many actual positives are found.

Low recall means the model fails to identify many true positive cases, resulting in many false negatives. Precision relates to false positives, accuracy is overall correctness.

🔧 Debug

expert

Debugging a text classification pipeline error

You run this code snippet for training a text classification model but get a ValueError: Found input variables with inconsistent numbers of samples. What is the cause?

ML Python

from sklearn.feature_extraction.text import CountVectorizer
from sklearn.linear_model import LogisticRegression
texts = ['good', 'bad', 'average']
labels = [1, 0]
vectorizer = CountVectorizer()
X = vectorizer.fit_transform(texts)
model = LogisticRegression()
model.fit(X, labels)

A LogisticRegression requires labels to be strings

B CountVectorizer cannot process single words

C The labels list length does not match the number of text samples

D fit_transform returns a dense matrix, but model expects sparse

💡 Hint

Check if the number of labels matches the number of texts.

The error occurs because texts has 3 samples but labels has only 2, causing mismatch in training data and labels.

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