Retraining strategies in ML Python - Cheat Sheet & Quick Revision

Recall & Review

beginner

What is the main goal of retraining a machine learning model?

The main goal is to update the model with new data so it stays accurate and adapts to changes over time.

intermediate

What is incremental retraining?

Incremental retraining means updating the model by training it on new data without starting from scratch, saving time and resources.

beginner

Why is it important to monitor model performance before retraining?

Monitoring helps detect when the model's accuracy drops, signaling that retraining is needed to maintain good predictions.

intermediate

What is the difference between full retraining and incremental retraining?

Full retraining trains the model from scratch using all data, while incremental retraining updates the existing model using only new data.

intermediate

Name one challenge of retraining machine learning models.

One challenge is avoiding overfitting to new data, which can reduce the model's ability to generalize to unseen data.

What triggers the need for retraining a machine learning model?

A When model performance decreases

B When the model is first created

C When the training data is perfect

D When the model is deployed

Retraining is needed when the model's performance drops, indicating it no longer fits the data well.

Which retraining strategy updates the model using only new data?

A Batch training

B Full retraining

C Transfer learning

D Incremental retraining

Incremental retraining updates the existing model with new data without retraining from scratch.

What is a risk of retraining a model too often?

A Model becomes outdated

B Model size decreases

C Model overfits to recent data

D Model stops learning

Frequent retraining can cause the model to overfit recent data and lose generalization.

Which of these is NOT a common retraining trigger?

A Model accuracy improves

B Data distribution changes

C New data becomes available

D Performance metrics degrade

Improved accuracy does not trigger retraining; retraining is triggered by performance drops or data changes.

What does full retraining involve?

A Training only on new data

B Training from scratch on all data

C Updating model weights manually

D Using a smaller dataset

Full retraining means training the model from scratch using the entire dataset.

Explain why and when a machine learning model should be retrained.

Think about what happens when the model sees new or different data.

Describe the difference between full retraining and incremental retraining.

Consider how much data is used and how long retraining takes.

Practice

1. What is the main purpose of retraining a machine learning model?

easy

A. To remove features from the model permanently

B. To make the model run faster on old data

C. To reduce the size of the training dataset

D. To update the model with new data and keep it accurate

Solution

1. Step 1: Understand retraining purpose

   Retraining updates the model using new data to maintain or improve accuracy.

2. Step 2: Compare options

   Options B, C, and D do not describe retraining goals; only A matches the purpose.

3. Final Answer:

   To update the model with new data and keep it accurate -> Option D

4. Quick Check:

   Retraining = update model with new data [OK]

Hint: Retraining means learning from new data to stay accurate [OK]

Common Mistakes:

• Thinking retraining speeds up old data processing
• Confusing retraining with data reduction
• Believing retraining removes features

2. Which of the following is the correct way to fine-tune a pretrained model in Python using TensorFlow?

easy

A. model.fit(new_data, epochs=5, initial_epoch=0)

B. model.train(new_data, epochs=5)

C. model.evaluate(new_data, epochs=5)

D. model.fit(new_data, epochs=5, initial_epoch=existing_epochs)

Solution

1. Step 1: Understand fine-tuning syntax

   Fine-tuning a pretrained model typically uses model.fit starting from epoch 0 after loading pretrained weights.

2. Step 2: Analyze options

   A uses non-existent method 'train'. C uses evaluate, which doesn't train and lacks epochs param. D risks initial_epoch >= epochs error. B correctly trains with model.fit from epoch 0.

3. Final Answer:

   model.fit(new_data, epochs=5, initial_epoch=0) -> Option A

4. Quick Check:

   Fine-tune = model.fit(new_data, epochs=N, initial_epoch=0) [OK]

Hint: Fine-tune pretrained with model.fit(new_data, epochs=N) [OK]

Common Mistakes:

• Using model.train instead of model.fit
• Setting initial_epoch=existing_epochs causing error
• Confusing evaluation with training

3. Consider this Python code snippet for incremental retraining:

old_accuracy = 0.85
model.fit(new_data, epochs=3)
new_accuracy = 0.88
improvement = new_accuracy - old_accuracy
print(improvement)

What will be the printed output?

medium

A. 0.85

B. 0.88

C. 0.03

D. 3

Solution

1. Step 1: Calculate improvement

   Improvement is new_accuracy minus old_accuracy: 0.88 - 0.85 = 0.03.

2. Step 2: Understand print output

   The print statement outputs the improvement variable, which is 0.03.

3. Final Answer:

   0.03 -> Option C

4. Quick Check:

   0.88 - 0.85 = 0.03 [OK]

Hint: Subtract old accuracy from new to find improvement [OK]

Common Mistakes:

• Printing new_accuracy instead of improvement
• Confusing epochs with accuracy values
• Printing old_accuracy by mistake

4. You have this code snippet for retraining a model incrementally:

model.fit(new_data, epochs=5)
model.fit(new_data, epochs=3, initial_epoch=5)

But the second fit call throws an error. What is the likely cause?

medium

A. initial_epoch should be less than epochs

B. initial_epoch cannot be greater than or equal to epochs

C. You must reset the model before second fit

D. epochs must be the same in both fit calls

Solution

1. Step 1: Understand initial_epoch and epochs relation

   initial_epoch must be less than epochs in the current fit call to define the training range properly.

2. Step 2: Analyze the error cause

   Here, initial_epoch=5 is greater than epochs=3, which is invalid and causes an error.

3. Final Answer:

   initial_epoch cannot be greater than or equal to epochs -> Option B

4. Quick Check:

   initial_epoch < epochs required [OK]

Hint: initial_epoch must be less than epochs in fit call [OK]

Common Mistakes:

• Thinking model reset is needed
• Assuming epochs must match both calls
• Setting initial_epoch greater or equal to epochs

5. You have a model trained on data from 2020. New data from 2023 shows some feature distributions changed. Which retraining strategy best balances training time and model accuracy?

hard

A. Fine-tuning the existing model on new data with a low learning rate

B. Full retraining from scratch with combined old and new data

C. Incremental retraining only on new data without old data

D. No retraining; use the old model as is

Solution

1. Step 1: Consider data changes and retraining options

   Feature distribution changed, so model needs updating but full retraining is costly.

2. Step 2: Evaluate strategies

   Full retraining (A) is accurate but slow. Incremental retraining (C) risks forgetting old data. No retraining (D) ignores changes. Fine-tuning (B) updates model efficiently with new data.

3. Final Answer:

   Fine-tuning the existing model on new data with a low learning rate -> Option A

4. Quick Check:

   Fine-tune balances accuracy and training time [OK]

Hint: Fine-tune to update model efficiently with new data [OK]

Common Mistakes:

• Choosing full retraining despite high cost
• Ignoring old data in incremental retraining
• Skipping retraining when data changes