Spam detection pipeline in NLP - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

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

NLP

from sklearn.feature_extraction.text import [1]

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

ATfidfVectorizer

BStandardScaler

CLabelEncoder

DCountVectorizer

Attempts:

3 left

💡 Hint

Common Mistakes

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

Using LabelEncoder which is for labels, not text features.

The TfidfVectorizer converts text data into numerical features based on term frequency-inverse document frequency, which is commonly used in spam detection.

2fill in blank

medium

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

NLP

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'

A1.0

B0.5

C0.1

D0.2

Attempts:

3 left

💡 Hint

Common Mistakes

Using 1.0 which means all data is test set, leaving no training data.

Using 0.5 which splits data evenly but reduces training data too much.

Using test_size=0.2 splits 20% of data for testing, which is a common practice to evaluate model performance.

3fill in blank

hard

Fix the error in the model training code by completing the missing classifier name.

NLP

from sklearn.naive_bayes import [1]
model = [1]()
model.fit(X_train, y_train)

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

AGaussianNB

BBernoulliNB

CMultinomialNB

DComplementNB

Attempts:

3 left

💡 Hint

Common Mistakes

Using GaussianNB which assumes continuous features, not suitable for text counts.

Using BernoulliNB which is for binary features, less common for tf-idf.

MultinomialNB is the best choice for text classification tasks like spam detection because it works well with discrete features such as word counts or tf-idf.

4fill in blank

hard

Fill both blanks to create a dictionary comprehension that maps each word to its length if the length is greater than 3.

NLP

word_lengths = {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 the word itself as value instead of its length.

Using less than '<' instead of greater than '>' in the condition.

The dictionary comprehension maps each word to its length using len(word) and filters words with length greater than 3 using >.

5fill in blank

hard

Fill all three blanks to create a filtered dictionary from data where keys are uppercase and values are positive.

NLP

filtered = [1]: [2] for k, v in data.items() if v [3] 0}

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

Ak.upper()

Bv

C>

Dk.lower()

Attempts:

3 left

💡 Hint

Common Mistakes

Using lowercase keys instead of uppercase.

Using '<' instead of '>' in the condition.

The dictionary comprehension uses k.upper() to convert keys to uppercase, keeps values v, and filters for values greater than zero using >.

Practice

1. What is the main purpose of a spam detection pipeline in NLP?

easy

A. To convert text messages into numbers and train a model to identify spam

B. To translate messages into different languages

C. To summarize long emails automatically

D. To generate new text messages based on spam examples

Solution

1. Step 1: Understand the role of a spam detection pipeline

   A spam detection pipeline processes text data to prepare it for a machine learning model that can classify messages as spam or not spam.

2. Step 2: Identify the key function

   The pipeline converts text into numbers (features) and trains a model to spot spam messages automatically.

3. Final Answer:

   To convert text messages into numbers and train a model to identify spam -> Option A

4. Quick Check:

   Spam detection pipeline = convert text + train model [OK]

Hint: Spam detection means turning text into numbers to train a model [OK]

Common Mistakes:

• Thinking it translates or summarizes text
• Confusing spam detection with text generation
• Ignoring the conversion of text to numbers

2. Which of the following code snippets correctly creates a simple spam detection pipeline using scikit-learn's Pipeline with a TfidfVectorizer and a LogisticRegression model?

easy

A. Pipeline([('vectorizer', TfidfVectorizer()), ('model', LogisticRegression())])

B. Pipeline(('vectorizer', TfidfVectorizer()), ('model', LogisticRegression()))

C. Pipeline({'vectorizer': TfidfVectorizer(), 'model': LogisticRegression()})

D. Pipeline(['vectorizer' = TfidfVectorizer(), 'model' = LogisticRegression()])

Solution

1. Step 1: Recall the correct syntax for scikit-learn Pipeline

   The Pipeline constructor expects a list of tuples, each tuple containing a name and a transformer or estimator.

2. Step 2: Check each option's syntax

   Pipeline([('vectorizer', TfidfVectorizer()), ('model', LogisticRegression())]) uses a list of tuples correctly. Other options use incorrect syntax like using '=' inside lists, passing tuples as separate arguments, or dictionary syntax.

3. Final Answer:

   Pipeline([('vectorizer', TfidfVectorizer()), ('model', LogisticRegression())]) -> Option A

4. Quick Check:

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

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

Common Mistakes:

• Using parentheses instead of brackets for the list
• Using dictionary syntax inside Pipeline
• Assigning steps with '=' inside a list

3. Given the following code, what will be the output of print(predictions) if the input messages are ["Win a free prize now", "Meeting at noon"] and the model predicts 1 for spam and 0 for not spam?

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

pipeline = Pipeline([
    ('vectorizer', TfidfVectorizer()),
    ('model', LogisticRegression())
])

# Assume pipeline is already trained
messages = ["Win a free prize now", "Meeting at noon"]
predictions = pipeline.predict(messages)
print(predictions)

medium

A. [0 1]

B. [1 0]

C. [1 1]

D. [0 0]

Solution

1. Step 1: Understand the input and model output

   The input has one spam-like message "Win a free prize now" and one normal message "Meeting at noon". The model labels spam as 1 and not spam as 0.

2. Step 2: Predict expected labels

   The first message is likely spam, so prediction is 1. The second is normal, so prediction is 0.

3. Final Answer:

   [1 0] -> Option B

4. Quick Check:

   Spam message = 1, normal message = 0 [OK]

Hint: Spam message predicts 1, normal message predicts 0 [OK]

Common Mistakes:

• Swapping labels 0 and 1
• Assuming both messages are spam
• Confusing output format with list of strings

4. Identify the error in this spam detection pipeline code and choose the correct fix:

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

pipeline = Pipeline([
    ('vectorizer', CountVectorizer),
    ('model', LogisticRegression())
])

pipeline.fit(train_messages, train_labels)

medium

A. Add parentheses to pipeline.fit() call

B. Replace LogisticRegression() with LogisticRegression

C. Remove the pipeline and train model directly

D. Change CountVectorizer to CountVectorizer() to create an instance

Solution

1. Step 1: Check the pipeline steps for correct instantiation

   CountVectorizer is a class and must be instantiated with parentheses to create an object.

2. Step 2: Identify the error and fix

   The code uses CountVectorizer without parentheses, causing an error. Adding parentheses fixes it.

3. Final Answer:

   Change CountVectorizer to CountVectorizer() to create an instance -> Option D

4. Quick Check:

   Instantiate classes with () in pipeline steps [OK]

Hint: Always instantiate transformers with () in pipeline steps [OK]

Common Mistakes:

• Forgetting parentheses after class names
• Confusing model and vectorizer instantiation
• Trying to remove pipeline instead of fixing syntax

5. You want to improve your spam detection pipeline by adding a step to remove common stop words before vectorizing. Which pipeline modification correctly adds this step using CountVectorizer with stop words removal?

hard

A. Pipeline([('stopwords', StopWordsRemover()), ('vectorizer', CountVectorizer()), ('model', LogisticRegression())])

B. Pipeline([('vectorizer', CountVectorizer()), ('stopwords', StopWordsRemover()), ('model', LogisticRegression())])

C. Pipeline([('vectorizer', CountVectorizer(stop_words='english')), ('model', LogisticRegression())])

D. Pipeline([('vectorizer', CountVectorizer(stop_words=None)), ('model', LogisticRegression())])

Solution

1. Step 1: Understand how to remove stop words in CountVectorizer

   CountVectorizer has a parameter stop_words which can be set to 'english' to remove common English stop words automatically.

2. Step 2: Check pipeline options for correct usage

   Pipeline([('vectorizer', CountVectorizer(stop_words='english')), ('model', LogisticRegression())]) correctly sets stop_words='english' inside CountVectorizer. Other options either use a non-existent StopWordsRemover step or set stop_words=None, which disables removal.

3. Final Answer:

   Pipeline([('vectorizer', CountVectorizer(stop_words='english')), ('model', LogisticRegression())]) -> Option C

4. Quick Check:

   Use stop_words='english' in CountVectorizer to remove stop words [OK]

Hint: Use stop_words='english' inside CountVectorizer [OK]

Common Mistakes:

• Trying to add a separate stop words remover step
• Setting stop_words to None disables removal
• Misplacing stop words removal after vectorizing