Machine learning concept in Intro to Computing - Practice Problems & Coding Challenges

Challenge - 5 Problems

🧠 Conceptual

intermediate

Understanding Overfitting in Machine Learning

Which of the following best describes overfitting in machine learning?

A The model performs well on training data but poorly on new, unseen data.

B The model performs poorly on both training and testing data due to lack of learning.

C The model ignores the training data and predicts random outputs.

D The model performs equally well on training and testing data.

💡 Hint

Think about when a model learns too much detail from the training data.

Overfitting happens when a model learns the training data too well, including noise, so it fails to generalize to new data.

🔍 Analysis

intermediate

Output of Simple Linear Regression Prediction

What is the output of the following Python code that fits a simple linear regression and predicts a value?

Intro to Computing

from sklearn.linear_model import LinearRegression
import numpy as np

X = np.array([[1], [2], [3], [4], [5]])
y = np.array([2, 4, 6, 8, 10])
model = LinearRegression().fit(X, y)
prediction = model.predict([[6]])
print(round(prediction[0], 2))

A 11.0

B 10.0

C 12.0

D 6.0

💡 Hint

Look at the pattern in y relative to X.

The data shows y = 2 * X, so predicting for X=6 gives 12.

❓ data_output

advanced

Confusion Matrix Interpretation

Given a binary classification confusion matrix below, what is the accuracy?

[[50, 10],
 [5, 35]]

A 80%

B 75%

C 90%

D 85%

💡 Hint

Accuracy = (True Positives + True Negatives) / Total samples.

Accuracy = (50 + 35) / (50 + 10 + 5 + 35) = 85 / 100 = 0.85 or 85%.

❓ visualization

advanced

Interpreting a Decision Tree Plot

Which statement best describes the role of the root node in a decision tree?

A It is the first decision point that splits the data based on the most important feature.

B It is the last node that gives the final prediction.

C It is a leaf node that contains the predicted class label.

D It is a node that randomly splits data without any criteria.

💡 Hint

Think about where the tree starts splitting data.

The root node is the top node where the first split happens, usually on the most important feature.

🔍 Analysis

expert

Identifying Error in K-Means Clustering Code

What error will the following code raise when run?

from sklearn.cluster import KMeans
import numpy as np

X = np.array([[1, 2], [1, 4], [1, 0], [10, 2], [10, 4], [10, 0]])
model = KMeans(n_clusters=2, random_state=0)
model.fit(X)
print(model.predict([[0, 0]]))

A TypeError: fit() missing 1 required positional argument

B No error, output is [0]

C ValueError: Number of clusters must be greater than number of samples

D AttributeError: 'KMeans' object has no attribute 'predict'

💡 Hint

Check if KMeans has a predict method and if input shapes are correct.

KMeans in sklearn has a predict method. The input shape is correct. So no error occurs and output is cluster label 0.