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

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

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

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

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