Machine Learning Basics — Practice MCQs for CCAT

Correct Answer: B — Artificial Intelligence

Machine Learning is a branch of AI that enables systems to learn from data without explicit programming.

Correct Answer: B — Labeled training data

Supervised learning uses labeled data where both input features and target outputs are provided.

Correct Answer: B — Customer segmentation/clustering

Clustering groups similar data points without predefined labels - a classic unsupervised learning task.

Correct Answer: B — Agent learning through rewards and penalties

Reinforcement learning trains agents to make decisions by receiving rewards or penalties for actions.

Correct Answer: B — Discrete categories/classes

Classification predicts discrete categorical labels like spam/not spam, cat/dog, etc.

Correct Answer: B — Continuous numerical values

Regression predicts continuous values like prices, temperatures, or sales figures.

Correct Answer: B — Model memorizes training data but fails on new data

Overfitting happens when a model learns noise in training data, performing well on training but poorly on unseen data.

Correct Answer: B — Model is too simple to capture patterns

Underfitting occurs when the model is too simple to capture the underlying patterns in the data.

Correct Answer: B — Evaluating model performance on different data splits

Cross-validation evaluates model performance by training and testing on different subsets of data.

Correct Answer: B — Input variable used for prediction

Features are input variables (attributes) that the model uses to make predictions.

Correct Answer: B — Train/fit the model parameters

Training set is used to train the model - the model learns patterns from this data.

Correct Answer: B — Evaluate model on unseen data

Test set evaluates how well the trained model generalizes to new, unseen data.

Correct Answer: B — Feature values to maximize information gain

Decision trees split data on feature values that best separate classes (maximize information gain/minimize impurity).

Correct Answer: B — Majority vote of k nearest neighbors

KNN classifies a point based on the majority class among its k nearest neighbors in feature space.

Correct Answer: B — Best-fit line to predict continuous values

Linear regression fits a line (or hyperplane) that minimizes the error between predictions and actual values.

Correct Answer: B — Binary classification

Despite its name, logistic regression is used for binary classification, predicting probabilities.

Correct Answer: B — Percentage of correct predictions

Accuracy is the ratio of correct predictions (both positive and negative) to total predictions.

Correct Answer: B — Proportion of true positives among predicted positives

Precision = True Positives / (True Positives + False Positives) - how many predicted positives are actually positive.

Correct Answer: B — Proportion of actual positives correctly identified

Recall = True Positives / (True Positives + False Negatives) - how many actual positives were correctly identified.

Correct Answer: B — Reducing bias increases variance and vice versa

Decreasing bias (making model more complex) typically increases variance (sensitivity to training data), and vice versa.