Types of ML (supervised, unsupervised, reinforcement) in ML Python - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to create a supervised learning model using scikit-learn.

ML Python

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

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

AKMeans

BLinearRegression

CQAgent

DPCA

Attempts:

3 left

💡 Hint

Common Mistakes

Choosing unsupervised or reinforcement models for supervised tasks.

LinearRegression is a supervised learning model used for predicting continuous values.

2fill in blank

medium

Complete the code to perform unsupervised clustering on data.

ML Python

from sklearn.cluster import [1]
model = [1](n_clusters=3)

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

ALinearRegression

BDecisionTree

CQAgent

DKMeans

Attempts:

3 left

💡 Hint

Common Mistakes

Using supervised models like LinearRegression for clustering.

KMeans is an unsupervised learning algorithm used for clustering data into groups.

3fill in blank

hard

Fix the error in the reinforcement learning agent initialization.

ML Python

class QAgent:
    def __init__(self, learning_rate=0.1, discount_factor=0.9):
        self.learning_rate = learning_rate
        self.discount_factor = discount_factor

agent = [1](learning_rate=0.1, discount_factor=0.9)

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

AQAgent

BKMeans

CLinearRegression

DPCA

Attempts:

3 left

💡 Hint

Common Mistakes

Using supervised or unsupervised model names instead of the agent class.

QAgent is the reinforcement learning agent class that needs to be instantiated.

4fill in blank

hard

Fill both blanks to complete the code that fits a supervised model and predicts.

ML Python

model = [1]()
model.[2](X_train, y_train)
predictions = model.predict(X_test)

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

ALinearRegression

Bfit

Ctransform

DKMeans

Attempts:

3 left

💡 Hint

Common Mistakes

Using transform() instead of fit() to train the model.

LinearRegression is the supervised model, and fit() trains it on data.

5fill in blank

hard

Fill all three blanks to create an unsupervised model, fit it, and transform data.

ML Python

from sklearn.decomposition import [1]
model = [1](n_components=2)
X_reduced = model.[2](X)
print(X_reduced.shape)  # Should print (n_samples, [3])

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

APCA

Bfit_transform

C2

Dfit

Attempts:

3 left

💡 Hint

Common Mistakes

Using fit() alone without transform or fit_transform.

PCA reduces data dimensions; fit_transform fits and transforms data; 2 is the number of components.

Practice

1. Which type of machine learning uses labeled data to teach the model?

easy

A. Supervised learning

B. Unsupervised learning

C. Reinforcement learning

D. Deep learning

Solution

1. Step 1: Understand labeled data meaning

   Labeled data means each example has a known answer or outcome.

2. Step 2: Match learning type with labeled data

   Supervised learning uses labeled data to train models to predict outcomes.

3. Final Answer:

   Supervised learning -> Option A

4. Quick Check:

   Supervised = labeled data [OK]

Hint: Labeled data means supervised learning [OK]

Common Mistakes:

• Confusing unsupervised with supervised
• Thinking reinforcement uses labeled data
• Mixing deep learning as a type here

2. Which of the following is the correct description of unsupervised learning?

easy

A. Learning from labeled data to predict outcomes

B. Finding patterns in data without labels

C. Learning actions to maximize rewards

D. Training deep neural networks

Solution

1. Step 1: Recall unsupervised learning definition

   Unsupervised learning finds hidden patterns or groups in data without labels.

2. Step 2: Match options with definition

   Finding patterns in data without labels correctly states it finds patterns without labels.

3. Final Answer:

   Finding patterns in data without labels -> Option B

4. Quick Check:

   Unsupervised = no labels [OK]

Hint: No labels means unsupervised learning [OK]

Common Mistakes:

• Choosing supervised for unlabeled data
• Confusing reinforcement with unsupervised
• Thinking deep learning is a type of ML here

3. Consider this Python code snippet for a reinforcement learning agent:

actions = ['left', 'right', 'up', 'down']
rewards = {'left': -1, 'right': 1, 'up': 0, 'down': 0}
current_action = 'right'
reward = rewards[current_action]
print(reward)

What will be the output?

medium

A. 0

B. -1

C. null

D. 1

Solution

1. Step 1: Identify the current action and reward mapping

   The current_action is 'right'. The rewards dictionary maps 'right' to 1.

2. Step 2: Print the reward for 'right'

   reward = rewards['right'] equals 1, so print outputs 1.

3. Final Answer:

   1 -> Option D

4. Quick Check:

   Reward for 'right' = 1 [OK]

Hint: Check dictionary key for current action [OK]

Common Mistakes:

• Confusing keys and values in dictionary
• Printing variable before assignment
• Assuming default reward 0

4. The following code is intended to perform supervised learning but has an error:

data = [([1, 2], 0), ([3, 4], 1), ([5, 6], 0)]
features, labels = zip(*data)
model = SomeModel()
model.train(features, labels)

What is the likely error?

medium

A. Features and labels are not separated correctly

B. Data format is incorrect for supervised learning

C. Model class SomeModel is undefined

D. train method should be fit instead

Solution

1. Step 1: Check variable unpacking

   features and labels are correctly unpacked from data using zip(*data).

2. Step 2: Identify model usage error

   SomeModel is not defined anywhere, so creating model = SomeModel() causes an error.

3. Final Answer:

   Model class SomeModel is undefined -> Option C

4. Quick Check:

   Undefined class causes error [OK]

Hint: Undefined classes cause errors at instantiation [OK]

Common Mistakes:

• Assuming train method is wrong without context
• Thinking data unpacking is incorrect
• Ignoring missing model definition

5. You want to build a system that learns to play a game by trying moves and getting points as feedback. Which type of machine learning should you use?

hard

A. Reinforcement learning

B. Supervised learning

C. Unsupervised learning

D. Clustering

Solution

1. Step 1: Understand the problem context

   The system learns by trying moves and receiving points (rewards) as feedback.

2. Step 2: Match learning type to feedback-based learning

   Reinforcement learning fits because it learns actions to maximize rewards from feedback.

3. Final Answer:

   Reinforcement learning -> Option A

4. Quick Check:

   Learning from rewards = Reinforcement [OK]

Hint: Learning from rewards means reinforcement learning [OK]

Common Mistakes:

• Choosing supervised because of 'learning'
• Confusing clustering with reinforcement
• Ignoring feedback as reward signal