Generative vs discriminative models in Prompt Engineering / GenAI - Metrics Comparison

Metrics & Evaluation - Generative vs discriminative models

Which metric matters for Generative vs Discriminative models and WHY

For discriminative models, metrics like accuracy, precision, recall, and F1-score matter most because these models focus on correctly classifying or predicting labels from input data.

For generative models, metrics that measure how well the model captures the data distribution are important. These include log-likelihood, perplexity, and Inception Score (for images). These metrics show how well the model can generate realistic new data.

In short, discriminative models are judged by how well they separate classes, while generative models are judged by how well they create or model data.

Confusion matrix example for a discriminative model

      Actual \ Predicted | Positive | Negative
      -------------------|----------|---------
      Positive           |    80    |   20
      Negative           |    10    |   90

      Total samples = 200
    

From this matrix:

• Precision = 80 / (80 + 10) = 0.89
• Recall = 80 / (80 + 20) = 0.80
• Accuracy = (80 + 90) / 200 = 0.85

Generative models do not use confusion matrices because they generate data rather than classify.

Precision vs Recall tradeoff with examples

Discriminative models: Imagine a spam email filter.

• High precision means most emails marked as spam really are spam (few good emails wrongly blocked).
• High recall means most spam emails are caught (few spam emails slip through).

Depending on what is worse (missing spam or blocking good emails), you choose to optimize precision or recall.

Generative models: The tradeoff is between quality and diversity of generated data.

• High quality means generated samples look very real.
• High diversity means generated samples cover many different types of data.

Improving one can reduce the other, so metrics like Inception Score balance this tradeoff.

What "good" vs "bad" metric values look like

Discriminative models:

• Good: Accuracy > 90%, Precision and Recall both > 85%
• Bad: Accuracy < 70%, Precision or Recall < 50%

Generative models:

• Good: High log-likelihood or low perplexity, Inception Score close to real data scores
• Bad: Low log-likelihood, high perplexity, generated data looks unrealistic or repetitive

Common pitfalls in metrics

• Accuracy paradox: High accuracy can be misleading if classes are imbalanced (e.g., 95% accuracy by always predicting the majority class).
• Data leakage: When test data leaks into training, metrics look better but model fails in real use.
• Overfitting indicators: Very high training accuracy but low test accuracy means the model memorizes instead of learning.
• Generative model pitfalls: Metrics like Inception Score can be fooled by models that generate limited but high-quality samples, missing diversity.

Self-check question

Your discriminative model has 98% accuracy but only 12% recall on fraud cases. Is it good for production? Why or why not?

Answer: No, it is not good. The model misses 88% of fraud cases (low recall), which is dangerous because many frauds go undetected. High accuracy is misleading here because fraud cases are rare. Improving recall is critical.

Key Result

Discriminative models focus on classification metrics like precision and recall, while generative models focus on data quality and diversity metrics like log-likelihood and Inception Score.

Practice

1. Which statement best describes a generative model in machine learning?

easy

A. It only works with labeled data for prediction.

B. It directly learns the boundary between classes for classification.

C. It learns how data is generated and can create new examples.

D. It ignores the data distribution and focuses on accuracy.

Solution

1. Step 1: Understand generative model purpose

   Generative models learn the underlying data distribution to generate new data points similar to the training data.

2. Step 2: Compare with discriminative models

   Discriminative models focus on learning the decision boundary between classes, not on generating data.

3. Final Answer:

   It learns how data is generated and can create new examples. -> Option C

4. Quick Check:

   Generative = create data [OK]

Hint: Generative models create data; discriminative separate classes [OK]

Common Mistakes:

• Confusing generative with discriminative models
• Thinking generative models only classify
• Assuming generative models ignore data distribution

2. Which of the following is the correct way to describe a discriminative model?

easy

A. It models the conditional probability of outputs given inputs.

B. It ignores labels and focuses on data generation.

C. It generates new data points similar to training data.

D. It models the joint probability of inputs and outputs.

Solution

1. Step 1: Define discriminative model behavior

   Discriminative models learn the conditional probability P(output|input), focusing on predicting labels from data.

2. Step 2: Contrast with generative models

   Generative models model the joint probability P(input, output) to generate data, which is not the case here.

3. Final Answer:

   It models the conditional probability of outputs given inputs. -> Option A

4. Quick Check:

   Discriminative = P(output|input) [OK]

Hint: Discriminative models predict labels from inputs [OK]

Common Mistakes:

• Mixing joint and conditional probabilities
• Thinking discriminative models generate data
• Confusing labels with data points

3. Consider the following Python code snippet using scikit-learn:

from sklearn.naive_bayes import GaussianNB
from sklearn.linear_model import LogisticRegression

X_train = [[1, 2], [2, 3], [3, 4], [4, 5]]
y_train = [0, 0, 1, 1]

model = GaussianNB()
model.fit(X_train, y_train)
predictions = model.predict([[2, 3]])
print(predictions)

What will be the output of this code?

medium

A. [1]

B. [0]

C. [0 1]

D. Error due to wrong model usage

Solution

1. Step 1: Identify model type and training data

   GaussianNB is a generative model that learns class distributions. Training data has two classes: 0 and 1.

2. Step 2: Predict class for input [2, 3]

   Input [2, 3] is closer to training points labeled 0 ([1,2],[2,3]) than to those labeled 1, so prediction is class 0.

3. Final Answer:

   [0] -> Option B

4. Quick Check:

   GaussianNB predicts class 0 for [2,3] [OK]

Hint: GaussianNB predicts class based on closest learned distribution [OK]

Common Mistakes:

• Assuming LogisticRegression is used instead
• Expecting multiple classes in output
• Thinking prediction causes error

4. The following code tries to train a discriminative model but has an error:

from sklearn.linear_model import LogisticRegression

X_train = [[1, 2], [2, 3], [3, 4]]
y_train = [0, 1]

model = LogisticRegression()
model.fit(X_train, y_train)

What is the error and how to fix it?

medium

A. Mismatch in number of samples and labels; fix by matching lengths.

B. LogisticRegression requires numeric labels; convert labels to numbers.

C. X_train must be a numpy array; convert list to array.

D. Model.fit() missing parameter; add sample weights.

Solution

1. Step 1: Check training data shapes

   X_train has 3 samples, but y_train has only 2 labels, causing mismatch error.

2. Step 2: Fix label length

   To fix, ensure y_train has 3 labels matching X_train samples, e.g., y_train = [0, 1, 0].

3. Final Answer:

   Mismatch in number of samples and labels; fix by matching lengths. -> Option A

4. Quick Check:

   Samples and labels count must match [OK]

Hint: Check if data and label counts match before training [OK]

Common Mistakes:

• Ignoring label count mismatch
• Assuming LogisticRegression needs label conversion
• Thinking data type causes error

5. You want to build a model that can both classify images of cats and dogs and also generate new realistic images of cats. Which approach should you choose?

hard

A. Use a clustering algorithm to separate and generate images.

B. Use a generative model like a Generative Adversarial Network (GAN) for both tasks.

C. Use a discriminative model like Logistic Regression for both tasks.

D. Use a discriminative model for classification and a generative model for image creation.

Solution

1. Step 1: Identify tasks and suitable models

   Classification is best done by discriminative models that separate classes well. Image generation requires generative models that learn data distribution.

2. Step 2: Combine models for both tasks

   Use a discriminative model for classifying cats vs dogs, and a generative model like GAN to create new cat images.

3. Final Answer:

   Use a discriminative model for classification and a generative model for image creation. -> Option D

4. Quick Check:

   Classification + generation = discriminative + generative [OK]

Hint: Classify with discriminative, generate with generative models [OK]

Common Mistakes:

• Using one model type for both tasks
• Confusing clustering with generation
• Ignoring model strengths for each task