Machine learning vs rule-based systems in AI for Everyone - Interactive Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to define a rule-based system that checks if a number is positive.

AI for Everyone

def is_positive(num):
    return num [1] 0

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

A>

B<

C==

D!=

Attempts:

3 left

💡 Hint

Common Mistakes

Using '<' instead of '>' will check for negative numbers.

The rule-based system checks if the number is greater than zero to decide if it is positive.

2fill in blank

medium

Complete the code to train a simple machine learning model using scikit-learn.

AI for Everyone

from sklearn.linear_model import LinearRegression
model = LinearRegression()
model.[1](X_train, y_train)

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

Apredict

Bfit

Ctransform

Dscore

Attempts:

3 left

💡 Hint

Common Mistakes

Using predict instead of fit will cause an error because the model is not trained yet.

The fit method trains the model on the training data.

3fill in blank

hard

Fix the error in the code that predicts labels using a trained model.

AI for Everyone

predictions = model.[1](X_test)

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

Apredict

Bfit

Ctransform

Dscore

Attempts:

3 left

💡 Hint

Common Mistakes

Using fit here will retrain the model instead of predicting.

The predict method is used to get predictions from the model on new data.

4fill in blank

hard

Fill both blanks to create a simple rule-based decision function.

AI for Everyone

def decide_action(temp):
    if temp [1] 30:
        return '[2]'
    else:
        return 'Stay inside'

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

A>

B<

CGo outside

DStay inside

Attempts:

3 left

💡 Hint

Common Mistakes

Using '<' instead of '>' will reverse the logic.

The function returns 'Go outside' if the temperature is greater than 30, otherwise 'Stay inside'.

5fill in blank

hard

Fill all three blanks to create a dictionary comprehension that maps words to their lengths if length is greater than 3.

AI for Everyone

lengths = { [1]: [2] for [3] in words if len([3]) > 3 }

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

Aword

Blen(word)

Ditem

Attempts:

3 left

💡 Hint

Common Mistakes

Using a variable name not defined in the loop will cause an error.

This dictionary comprehension creates a dictionary where keys are words and values are their lengths, but only for words longer than 3 characters.

Practice

1. Which of the following best describes a machine learning system compared to a rule-based system?

easy

A. It only works with simple, clear instructions.

B. It follows fixed rules without change.

C. It learns from data and adapts over time.

D. It cannot improve after deployment.

Solution

1. Step 1: Understand machine learning characteristics

   Machine learning systems learn from examples and improve with more data.

2. Step 2: Compare with rule-based systems

   Rule-based systems follow fixed instructions and do not adapt.

3. Final Answer:

   It learns from data and adapts over time. -> Option C

4. Quick Check:

   Machine learning = adapts [OK]

Hint: Machine learning adapts; rule-based does not [OK]

Common Mistakes:

• Confusing fixed rules with learning
• Thinking rule-based systems adapt
• Assuming machine learning cannot improve

2. Which syntax correctly describes a rule-based system?

easy

A. train_model(data) to predict temperature

B. if temperature > 30 then turn_on_fan() else turn_off_fan()

C. learn_from_data(data) to adjust fan speed

D. update_rules_based_on_feedback()

Solution

1. Step 1: Identify rule-based syntax

   Rule-based systems use fixed if-then rules like 'if temperature > 30 then turn_on_fan()'.

2. Step 2: Check other options

   Options A, C, and D describe learning or updating, which are machine learning concepts.

3. Final Answer:

   if temperature > 30 then turn_on_fan() else turn_off_fan() -> Option B

4. Quick Check:

   Rule-based = fixed if-then rules [OK]

Hint: Rule-based uses fixed if-then rules [OK]

Common Mistakes:

• Confusing learning functions with rules
• Choosing options that imply adaptation
• Ignoring fixed condition-action format

3. Consider this simple system:
rules = {'hot': 'turn_on_ac', 'cold': 'turn_on_heater'}
def apply_rule(temp):
    if temp > 25:
        return rules['hot']
    else:
        return rules['cold']
print(apply_rule(30))
What will this print?

medium

A. Error

B. turn_on_heater

C. null

D. turn_on_ac

Solution

1. Step 1: Analyze the input and condition

   Input temperature is 30, which is greater than 25, so the 'hot' rule applies.

2. Step 2: Determine the returned action

   The function returns rules['hot'], which is 'turn_on_ac'.

3. Final Answer:

   turn_on_ac -> Option D

4. Quick Check:

   Temp 30 > 25 -> 'turn_on_ac' [OK]

Hint: Check condition then pick matching rule [OK]

Common Mistakes:

• Choosing 'turn_on_heater' ignoring condition
• Assuming function returns null
• Thinking code causes error

4. This code tries to use a rule-based system but has a bug:
rules = {'hot': 'turn_on_ac', 'cold': 'turn_on_heater'}
def apply_rule(temp):
    if temp > 25:
        return rules['hot']
    elif temp <= 25:
        return rules['cold']
print(apply_rule(25))
What is the bug and how to fix it?

medium

A. Bug: 'elif' should be 'else'; fix by replacing 'elif' with 'else'.

B. Bug: Missing rule for temp=25; fix by adding 'temp == 25' rule.

C. Bug: KeyError on 'cold'; fix by adding 'cold' key to rules.

D. Bug: Function does not return anything; fix by adding return statement.

Solution

1. Step 1: Identify condition overlap

   The code uses 'elif temp <= 25', but temp=25 matches this condition. However, since 'if temp > 25' fails implies 'temp <= 25', the elif is redundant.

2. Step 2: Check if 'elif' is necessary

   Since the first condition is 'temp > 25', the else branch can cover all other cases, so 'else' is simpler and clearer.

3. Final Answer:

   Bug: 'elif' should be 'else'; fix by replacing 'elif' with 'else'. -> Option A

4. Quick Check:

   Use else for remaining cases [OK]

Hint: Use else for all other cases, not elif [OK]

Common Mistakes:

• Thinking temp=25 is missing
• Assuming KeyError occurs
• Believing function lacks return

5. You want to build a system that detects spam emails. The rules for spam change often and new patterns appear regularly. Which approach is best and why?

hard

A. Use machine learning because it can learn new spam patterns from data.

B. Use a rule-based system because rules are easy to write and fixed.

C. Use a rule-based system because it never makes mistakes.

D. Use machine learning because it requires no data to work.

Solution

1. Step 1: Understand problem requirements

   Spam patterns change often, so fixed rules will become outdated quickly.

2. Step 2: Choose approach based on adaptability

   Machine learning can learn from new data and adapt to new spam patterns automatically.

3. Final Answer:

   Use machine learning because it can learn new spam patterns from data. -> Option A

4. Quick Check:

   Changing patterns = machine learning [OK]

Hint: Changing rules? Choose machine learning [OK]

Common Mistakes:

• Choosing rule-based for changing patterns
• Thinking machine learning needs no data
• Assuming rule-based systems never err