Logistic regression for text in NLP - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to import the logistic regression model from scikit-learn.

NLP

from sklearn.linear_model import [1]

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

ALogisticRegression

BLinearRegression

CDecisionTreeClassifier

DKNeighborsClassifier

Attempts:

3 left

💡 Hint

Common Mistakes

Choosing LinearRegression which is for regression, not classification.

Confusing with other classifiers like DecisionTreeClassifier.

The logistic regression model class in scikit-learn is called LogisticRegression. It is used for classification tasks.

2fill in blank

medium

Complete the code to convert text data into numerical features using CountVectorizer.

NLP

from sklearn.feature_extraction.text import [1]

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

ACountVectorizer

BDictVectorizer

CTfidfVectorizer

DLabelEncoder

Attempts:

3 left

💡 Hint

Common Mistakes

Using TfidfVectorizer when the task asks specifically for count-based features.

Using LabelEncoder which is for labels, not text features.

CountVectorizer converts text documents into a matrix of token counts, which is suitable for logistic regression input.

3fill in blank

hard

Fix the error in the code to train the logistic regression model on vectorized text data.

NLP

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

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

Apredict

Bscore

Ctransform

Dfit

Attempts:

3 left

💡 Hint

Common Mistakes

Using predict instead of fit causes errors because the model is not trained yet.

Using transform which is a method for vectorizers, not models.

The fit method trains the model using the training features and labels.

4fill in blank

hard

Fill both blanks to create a dictionary comprehension that maps words to their counts only if the count is greater than 2.

NLP

word_counts = {word: [1] for word, count in counts.items() if count [2] 2}

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

Acount

B>

C<

Dword

Attempts:

3 left

💡 Hint

Common Mistakes

Using word as the value instead of count.

Using < instead of > in the condition.

The dictionary comprehension keeps words with counts greater than 2, so the value is count and the condition is >.

5fill in blank

hard

Fill all three blanks to create a dictionary of word lengths for words longer than 4 characters.

NLP

lengths = [1]: [2] for [3] in words if len([3]) > 4}

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

Aword

Blen(word)

Ditem

Attempts:

3 left

💡 Hint

Common Mistakes

Using item as the loop variable but not matching keys and values.

Using len(item) without defining item.

The dictionary keys are words, values are their lengths, and the loop variable is word.

Practice

1. What is the main purpose of logistic regression when applied to text data?

easy

A. To count the number of words in a text

B. To generate new text sentences

C. To classify text into categories like positive or negative

D. To translate text from one language to another

Solution

1. Step 1: Understand logistic regression's role in text

   Logistic regression is a method used to classify data into categories based on input features.

2. Step 2: Apply to text classification

   When applied to text, logistic regression predicts categories like positive or negative sentiment.

3. Final Answer:

   To classify text into categories like positive or negative -> Option C

4. Quick Check:

   Logistic regression classifies text [OK]

Hint: Logistic regression predicts categories, not generates text [OK]

Common Mistakes:

• Confusing classification with text generation
• Thinking logistic regression translates languages
• Assuming it only counts words

2. Which Python library is commonly used to convert text into numbers before applying logistic regression?

easy

A. CountVectorizer

B. matplotlib

C. pandas

D. seaborn

Solution

1. Step 1: Identify text to number conversion tools

   CountVectorizer is a tool that converts text into a matrix of token counts, suitable for models.

2. Step 2: Match with logistic regression preprocessing

   Before logistic regression, text must be numeric; CountVectorizer is commonly used for this.

3. Final Answer:

   CountVectorizer -> Option A

4. Quick Check:

   Text to numbers = CountVectorizer [OK]

Hint: CountVectorizer turns words into numbers for models [OK]

Common Mistakes:

• Choosing plotting libraries like matplotlib
• Confusing data frame libraries like pandas
• Selecting visualization tools like seaborn

3. What will be the output of this code snippet?

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

texts = ['good movie', 'bad movie']
labels = [1, 0]

vectorizer = CountVectorizer()
X = vectorizer.fit_transform(texts)
model = LogisticRegression()
model.fit(X, labels)
pred = model.predict(vectorizer.transform(['good movie']))
print(pred)

medium

A. [0]

B. [1]

C. [1, 0]

D. Error: model not trained

Solution

1. Step 1: Understand training data and labels

   Texts 'good movie' labeled 1 (positive), 'bad movie' labeled 0 (negative).

2. Step 2: Predict on 'good movie'

   Model trained on these examples predicts label for 'good movie' as 1.

3. Final Answer:

   [1] -> Option B

4. Quick Check:

   Prediction for 'good movie' = 1 [OK]

Hint: Model predicts label matching training example [OK]

Common Mistakes:

• Assuming prediction returns multiple labels
• Thinking model is untrained causing error
• Confusing label 0 and 1

4. Identify the error in this code snippet for logistic regression on text:

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

texts = ['happy', 'sad']
labels = [1, 0]

vectorizer = CountVectorizer()
X = vectorizer.fit_transform(texts)
model = LogisticRegression()
model.fit(texts, labels)

medium

A. model.fit should use numeric features, not raw texts

B. CountVectorizer is not imported

C. fit_transform should be called on labels

D. Labels should be strings, not integers

Solution

1. Step 1: Check input to model.fit

   Model expects numeric features, but code passes raw text strings.

2. Step 2: Correct usage of vectorized data

   Must pass X (vectorized text) to model.fit, not original texts.

3. Final Answer:

   model.fit should use numeric features, not raw texts -> Option A

4. Quick Check:

   Model needs numbers, not raw text [OK]

Hint: Pass vectorized text, not raw strings, to model.fit [OK]

Common Mistakes:

• Passing raw text instead of vectorized data
• Confusing labels data type requirements
• Ignoring import statements

5. You trained a logistic regression model on text data using CountVectorizer. When testing on new sentences, the model predicts only one class for all inputs. What is the best way to improve the model's performance?

hard

A. Change logistic regression to linear regression

B. Remove CountVectorizer and use raw text directly

C. Use fewer training examples to avoid overfitting

D. Increase the number of training examples and use n-grams in CountVectorizer

Solution

1. Step 1: Understand cause of single-class prediction

   Model may be underfitting due to limited data or simple features.

2. Step 2: Improve feature richness and data size

   Adding more training examples and using n-grams captures more context, improving model learning.

3. Final Answer:

   Increase the number of training examples and use n-grams in CountVectorizer -> Option D

4. Quick Check:

   More data + better features = better model [OK]

Hint: More data and richer features improve classification [OK]

Common Mistakes:

• Removing vectorizer loses numeric input
• Reducing data worsens model
• Confusing regression types