First ML prediction (linear regression) in ML Python - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

Output of simple linear regression prediction

What is the predicted value of y for x = 4 using the model y = 2x + 1?

ML Python

def predict(x):
    return 2 * x + 1

result = predict(4)
print(result)

A 8

B 7

C 9

D 10

❓ Model Choice

intermediate

Choosing the right model for linear data

You have data points that form a straight line. Which model is best to predict new points?

A Neural network with many layers

B Decision tree

C K-means clustering

D Linear regression

❓ Metrics

advanced

Interpreting mean squared error (MSE)

If a linear regression model has an MSE of 0.01 on test data, what does this mean?

A The average squared difference between predicted and actual values is 0.01

B The model predicts perfectly with no errors

C The model's predictions are off by 1 on average

D The model's accuracy is 1%

🔧 Debug

advanced

Why does this linear regression code fail?

What error will this code produce? from sklearn.linear_model import LinearRegression model = LinearRegression() X = [1, 2, 3, 4] y = [2, 4, 6, 8] model.fit(X, y)

ML Python

from sklearn.linear_model import LinearRegression
model = LinearRegression()
X = [1, 2, 3, 4]
y = [2, 4, 6, 8]
model.fit(X, y)

A No error, code runs fine

B ValueError because X must be 2D array

C TypeError because y is not a numpy array

D NameError because LinearRegression is not imported

🧠 Conceptual

expert

Effect of learning rate in linear regression training

What happens if the learning rate in gradient descent for linear regression is set too high?

A The model may fail to converge and loss can increase or oscillate

B The model will converge faster without any issues

C The model will ignore the learning rate and converge normally

D The model will always find the global minimum immediately