Why regression predicts continuous values in ML Python - Challenge Your Understanding

Challenge - 5 Problems

🧠 Conceptual

intermediate

Why does regression output continuous values?

Regression models are used to predict values. What is the main reason regression predicts continuous values instead of categories?

A Because regression models estimate a relationship that maps inputs to a range of real numbers.

B Because regression models classify data points into fixed groups.

C Because regression models only work with binary outputs.

D Because regression models use clustering to group similar data.

❓ Predict Output

intermediate

Output of a simple linear regression prediction

What is the output of this code predicting a value using a simple linear regression model?

ML Python

from sklearn.linear_model import LinearRegression
import numpy as np

X = np.array([[1], [2], [3], [4]])
y = np.array([2, 4, 6, 8])
model = LinearRegression().fit(X, y)
prediction = model.predict(np.array([[5]]))
print(prediction[0])

A 8.0

B 12.0

C 5.0

D 10.0

❓ Model Choice

advanced

Choosing a model for continuous value prediction

You want to predict house prices based on features like size and location. Which model type is best suited for this task?

A Logistic Regression

B Linear Regression

C K-Means Clustering

D Decision Tree Classifier

❓ Hyperparameter

advanced

Effect of regularization on regression output

In a regression model, what is the effect of increasing the regularization strength (like alpha in Ridge regression) on the predicted continuous values?

A It shrinks coefficients, leading to simpler models and potentially less extreme predictions.

B It increases the number of features used, making predictions more complex.

C It converts continuous outputs into categories automatically.

D It removes the intercept term, forcing predictions through zero.

❓ Metrics

expert

Choosing the right metric for continuous value prediction

Which metric is most appropriate to evaluate the accuracy of a regression model predicting continuous values?

A F1 Score

B Accuracy Score

C Mean Squared Error (MSE)

D Confusion Matrix