Generative vs discriminative models in Prompt Engineering / GenAI - Model Approaches Compared

Model Pipeline - Generative vs discriminative models

This pipeline compares two types of models: generative models that learn how data is made, and discriminative models that learn to tell categories apart.

Data Flow - 5 Stages

1Input Data

1000 rows x 3 columns→Raw data with features and labels→1000 rows x 3 columns

[Feature1=5.1, Feature2=3.5, Label=ClassA]

↓

2Generative Model Training

1000 rows x 3 columns→Model learns joint probability of features and labels→Model parameters representing data distribution

Learned means and variances for each class

↓

3Discriminative Model Training

1000 rows x 3 columns→Model learns decision boundary between classes→Model parameters for classification

Weights for logistic regression separating classes

↓

4Prediction with Generative Model

1 row x 2 columns→Calculate probability of each class given features→Class probabilities

[ClassA: 0.7, ClassB: 0.3]

↓

5Prediction with Discriminative Model

1 row x 2 columns→Calculate class probability directly→Class probabilities

[ClassA: 0.8, ClassB: 0.2]

Training Trace - Epoch by Epoch

Loss
0.7 |*       
0.6 | **     
0.5 |  ***   
0.4 |   **** 
0.3 |    *****
    +---------
     1 2 3 4 5 Epochs

Epoch	Loss ↓	Accuracy ↑	Observation
1	0.65	0.60	Both models start with moderate loss and accuracy.
2	0.50	0.72	Loss decreases and accuracy improves as models learn.
3	0.40	0.80	Clear improvement; discriminative model may learn faster.
4	0.35	0.85	Loss continues to decrease; accuracy rises steadily.
5	0.30	0.88	Models converge with good accuracy.

Prediction Trace - 4 Layers

Layer 1: Input features

Layer 2: Generative model calculates likelihood

Layer 3: Discriminative model calculates probability

Layer 4: Final decision

Model Quiz - 3 Questions

Which model type learns how data is generated?

A Discriminative model

B Generative model

C Neither model

D Both models

Key Insight

Generative models learn the full data distribution and can generate data, while discriminative models focus on drawing boundaries to classify data. Both improve with training as loss decreases and accuracy increases.

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