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.

Practice

1. What is the main purpose of machine learning in data science?

easy

A. To write complex code manually for every task

B. To replace all human decision-making instantly

C. To store large amounts of data without analysis

D. To let computers learn from data and make predictions

Solution

1. Step 1: Understand machine learning goal

   Machine learning helps computers learn patterns from data instead of hard coding rules.

2. Step 2: Compare options to goal

   Only To let computers learn from data and make predictions describes learning from data to predict or decide, which matches the goal.

3. Final Answer:

   To let computers learn from data and make predictions -> Option D

4. Quick Check:

   Machine learning = learning from data [OK]

Hint: Machine learning means learning from data, not manual coding [OK]

Common Mistakes:

• Thinking machine learning is just storing data
• Believing it replaces all human decisions immediately
• Confusing it with writing manual code

2. Which of the following is the correct order of steps in a typical machine learning process?

easy

A. Create model, train model, predict results

B. Predict results, train model, create model

C. Train model, create model, predict results

D. Predict results, create model, train model

Solution

1. Step 1: Recall machine learning workflow

   First, create a model structure, then train it with data, finally use it to predict.

2. Step 2: Match steps to options

   Create model, train model, predict results correctly orders: create model, train model, predict results.

3. Final Answer:

   Create model, train model, predict results -> Option A

4. Quick Check:

   Order = create, train, predict [OK]

Hint: Model must be created before training and predicting [OK]

Common Mistakes:

• Trying to train before creating the model
• Predicting before training the model
• Mixing the order of steps

3. Consider this Python code snippet using scikit-learn:

from sklearn.linear_model import LinearRegression
model = LinearRegression()
X_train = [[1], [2], [3], [4]]
y_train = [2, 4, 6, 8]
model.fit(X_train, y_train)
print(model.predict([[5]]))

What will be the output?

medium

A. [10.]

B. [5.]

C. [8.]

D. Error: model not trained

Solution

1. Step 1: Understand training data pattern

   X_train and y_train show y = 2 * x (e.g., 1->2, 2->4, 3->6, 4->8).

2. Step 2: Predict for input 5

   Using the learned pattern, prediction for 5 is 2 * 5 = 10.

3. Final Answer:

   [10.] -> Option A

4. Quick Check:

   Prediction = 2 * 5 = 10 [OK]

Hint: Check training pattern, then apply to new input [OK]

Common Mistakes:

• Ignoring training data pattern
• Confusing input and output values
• Assuming model is untrained

4. Identify the error in this machine learning code snippet:

from sklearn.tree import DecisionTreeClassifier
model = DecisionTreeClassifier()
X_train = [[0], [1], [2]]
y_train = [0, 1, 1]
model.fit(X_train)
print(model.predict([[1]]))

medium

A. Incorrect import statement

B. Missing target labels in fit method

C. Predict method called before fit

D. X_train has wrong data type

Solution

1. Step 1: Check fit method parameters

   fit() requires both features (X_train) and labels (y_train), but only X_train is given.

2. Step 2: Identify error cause

   Missing y_train causes error because model doesn't know target labels to learn.

3. Final Answer:

   Missing target labels in fit method -> Option B

4. Quick Check:

   fit() needs X and y [OK]

Hint: fit() always needs features and labels [OK]

Common Mistakes:

• Forgetting to pass y_train to fit()
• Thinking predict can run before fit
• Assuming import is wrong

5. You have a dataset with missing values and want to build a machine learning model. Which step should you take before training the model?

hard

A. Only use the missing values as input features

B. Train the model directly on raw data

C. Remove or fill missing values to clean the data

D. Ignore missing values as models handle them automatically

Solution

1. Step 1: Understand impact of missing data

   Missing values can confuse models and reduce accuracy if not handled.

2. Step 2: Choose proper data cleaning step

   Removing or filling missing values (imputation) prepares data for better training.

3. Final Answer:

   Remove or fill missing values to clean the data -> Option C

4. Quick Check:

   Clean data before training [OK]

Hint: Always clean missing data before training [OK]

Common Mistakes:

• Training on raw data with missing values
• Assuming models ignore missing data
• Using missing values as features without cleaning