Handling imbalanced text data in NLP - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to import the class used for oversampling minority classes.

NLP

from imblearn.over_sampling import [1]

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

ATfidfVectorizer

BRandomOverSampler

CSMOTE

DCountVectorizer

Attempts:

3 left

💡 Hint

Common Mistakes

Confusing vectorizers with oversampling methods.

Using RandomOverSampler instead of SMOTE.

The SMOTE class is commonly used to oversample minority classes in imbalanced datasets.

2fill in blank

medium

Complete the code to convert text data into numerical features using TF-IDF.

NLP

from sklearn.feature_extraction.text import [1]
tfidf = [1](stop_words='english')
X = tfidf.fit_transform(texts)

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

ACountVectorizer

BTfidfVectorizer

CSMOTE

DRandomOverSampler

Attempts:

3 left

💡 Hint

Common Mistakes

Using CountVectorizer which only counts words.

Trying to use oversampling classes here.

TfidfVectorizer converts text into numerical features by calculating term frequency-inverse document frequency.

3fill in blank

hard

Fix the error in applying SMOTE to the feature matrix X and labels y.

NLP

smote = SMOTE(random_state=42)
X_resampled, y_resampled = smote.fit_resample([1], y)

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

AX

By

Csmote

Drandom_state

Attempts:

3 left

💡 Hint

Common Mistakes

Swapping the order of X and y.

Passing the SMOTE object instead of data.

The fit_resample method takes the feature matrix X and labels y. Passing y as the first argument is incorrect.

4fill in blank

hard

Fill both blanks to create a balanced dataset using RandomOverSampler and convert text to features.

NLP

from imblearn.over_sampling import [1]
from sklearn.feature_extraction.text import [2]
ros = [1](random_state=0)
tfidf = [2](stop_words='english')
X = tfidf.fit_transform(texts)
X_resampled, y_resampled = ros.fit_resample(X, y)

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

ARandomOverSampler

BSMOTE

CTfidfVectorizer

DCountVectorizer

Attempts:

3 left

💡 Hint

Common Mistakes

Mixing SMOTE with RandomOverSampler.

Using CountVectorizer instead of TfidfVectorizer.

RandomOverSampler is used to oversample minority classes. TfidfVectorizer converts text to numerical features.

5fill in blank

hard

Fill all three blanks to create a pipeline that balances data and trains a logistic regression model.

NLP

from imblearn.pipeline import Pipeline
from sklearn.linear_model import [1]
from imblearn.over_sampling import [2]
from sklearn.feature_extraction.text import [3]
pipeline = Pipeline([
    ('vectorizer', [3](stop_words='english')),
    ('oversample', [2](random_state=42)),
    ('classifier', [1]())
])

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

ALogisticRegression

BRandomOverSampler

CTfidfVectorizer

DSMOTE

Attempts:

3 left

💡 Hint

Common Mistakes

Using SMOTE instead of RandomOverSampler in this pipeline.

Confusing CountVectorizer with TfidfVectorizer.

This pipeline vectorizes text with TfidfVectorizer, balances classes with RandomOverSampler, and trains a LogisticRegression model.

Practice

1. What is the main problem caused by imbalanced text data in machine learning models?

easy

A. The model may become biased towards the majority class

B. The model will always have perfect accuracy

C. The model will ignore all classes

D. The model will run faster

Solution

1. Step 1: Understand class imbalance impact

   Imbalanced data means one class has many more examples than others, causing the model to favor that class.

2. Step 2: Recognize bias effect

   This bias leads to poor performance on minority classes, reducing fairness and accuracy for those classes.

3. Final Answer:

   The model may become biased towards the majority class -> Option A

4. Quick Check:

   Imbalanced data causes bias = D [OK]

Hint: Imbalance means bias toward bigger class [OK]

Common Mistakes:

• Thinking imbalance improves accuracy
• Assuming model ignores all classes
• Believing imbalance speeds up training

2. Which Python library function is commonly used to perform upsampling on imbalanced text data?

easy

A. numpy.dot

B. pandas.read_csv

C. sklearn.utils.resample

D. matplotlib.plot

Solution

1. Step 1: Identify upsampling tool

   Upsampling means increasing minority class samples, and sklearn.utils.resample is designed for this.

2. Step 2: Eliminate unrelated functions

   pandas.read_csv loads data, numpy.dot does matrix multiplication, matplotlib.plot draws graphs, so they don't upsample.

3. Final Answer:

   sklearn.utils.resample -> Option C

4. Quick Check:

   Upsampling uses sklearn.utils.resample = A [OK]

Hint: Upsample with sklearn.utils.resample [OK]

Common Mistakes:

• Confusing data loading with upsampling
• Using plotting or math functions for sampling
• Not knowing sklearn utilities

3. Given this Python code snippet for downsampling the majority class in text data, what will be the length of downsampled_majority?

from sklearn.utils import resample
majority = ['a'] * 1000
minority = ['b'] * 100

downsampled_majority = resample(majority, replace=False, n_samples=len(minority), random_state=42)
print(len(downsampled_majority))

medium

A. 1000

B. 42

C. 1100

D. 100

Solution

1. Step 1: Understand resample parameters

   resample is called with n_samples equal to length of minority (100), so it will pick 100 samples from majority.

2. Step 2: Check replace and output length

   replace=False means no duplicates, so output length equals n_samples, which is 100.

3. Final Answer:

   100 -> Option D

4. Quick Check:

   Downsampled length = minority size = 100 [OK]

Hint: Downsample size matches minority length [OK]

Common Mistakes:

• Assuming output length equals original majority size
• Confusing random_state with sample size
• Ignoring n_samples parameter

4. Identify the error in this code snippet that tries to balance imbalanced text data by upsampling minority class:

from sklearn.utils import resample
minority = ['text1', 'text2']
upsampled_minority = resample(minority, replace=True, n_samples=5)
print(len(upsampled_minority))

medium

A. No error; code runs correctly and prints 5

B. Missing random_state parameter causes error

C. replace=True is invalid for resample

D. n_samples must be less than original minority size

Solution

1. Step 1: Check resample parameters

   replace=True allows sampling with replacement, so n_samples can be larger than original minority size.

2. Step 2: Verify code behavior

   random_state is optional; code runs fine and prints length 5 as expected.

3. Final Answer:

   No error; code runs correctly and prints 5 -> Option A

4. Quick Check:

   Upsampling with replacement works = A [OK]

Hint: replace=True allows larger sample size [OK]

Common Mistakes:

• Thinking random_state is mandatory
• Believing n_samples must be smaller
• Confusing replace parameter usage

5. You have a text classification dataset with 90% class A and 10% class B. After upsampling class B to balance the data, which metric should you check to ensure your model performs well on both classes?

hard

A. Accuracy only

B. Precision and recall for each class

C. Training time

D. Number of epochs

Solution

1. Step 1: Understand metric importance

   Accuracy can be misleading with imbalanced data; precision and recall show performance per class.

2. Step 2: Choose metrics for balanced evaluation

   Precision and recall help check if model correctly identifies minority class without many false positives or negatives.

3. Final Answer:

   Precision and recall for each class -> Option B

4. Quick Check:

   Balanced data needs precision & recall check = C [OK]

Hint: Check precision and recall, not just accuracy [OK]

Common Mistakes:

• Relying only on accuracy
• Ignoring class-wise metrics
• Focusing on training time or epochs