Handling imbalanced text data in NLP - Cheat Sheet & Quick Revision

Recall & Review

beginner

What does 'imbalanced text data' mean in machine learning?

Imbalanced text data means some classes or categories have many more examples than others, making it hard for models to learn equally well from all classes.

beginner

Name one simple method to handle imbalanced text data.

One simple method is 'oversampling' the minority class by duplicating its examples to balance the dataset.

intermediate

What is 'undersampling' and when is it used?

Undersampling means reducing the number of examples in the majority class to balance the dataset. It is used when the majority class is very large and can be safely reduced without losing important information.

intermediate

How can synthetic data generation help with imbalanced text data?

Synthetic data generation creates new, artificial examples of the minority class (like using SMOTE) to increase its size and help the model learn better.

beginner

Why is accuracy not a good metric for imbalanced text classification?

Accuracy can be misleading because a model can predict the majority class all the time and still get high accuracy, ignoring the minority class performance.

What is a common problem when training on imbalanced text data?

A The model ignores minority classes

B The model trains faster

C The model always predicts minority classes

D The model requires no preprocessing

Models tend to ignore minority classes because they see fewer examples, leading to poor predictions for those classes.

Which technique involves creating new examples for the minority class?

A Undersampling

B Oversampling

C Feature scaling

D Synthetic data generation

Synthetic data generation creates new artificial examples to increase minority class size.

Why might undersampling be risky?

A It can remove useful data from the majority class

B It increases dataset size

C It duplicates minority class data

D It always improves accuracy

Removing too many majority class examples can lose important information and hurt model performance.

Which metric is better than accuracy for imbalanced text classification?

A F1-score

B Recall

C All of the above

D Precision

Precision, recall, and F1-score give better insight into minority class performance.

What does oversampling do?

A Removes majority class examples

B Duplicates minority class examples

C Creates synthetic majority class data

D Normalizes text data

Oversampling duplicates or adds more examples to the minority class to balance the dataset.

Explain why handling imbalanced text data is important and describe two methods to address it.

Think about how models learn better with balanced examples.

Describe how synthetic data generation can help with imbalanced text data and name a technique used for it.

It’s like making new examples similar to existing minority data.

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