Supervised Learning Flashcards
7 cards from real Machine Learning practice questions. Tap to flip, then mark Knew It or Still Learning — missed cards come back until you master them.
Read the first 7 Supervised Learning flashcards as text
What is 'data leakage' in a supervised learning pipeline?
Answer: Information from the test set inadvertently influencing model training
Data leakage occurs when information outside the training set (e.g., from the test set) is used during training, causing artificially inflated performance metrics.
Which of the following is NOT a hyperparameter of a Random Forest model?
Answer: Learned feature weights of the trees
Learned feature weights are parameters determined by the training process, not hyperparameters set before training.
When using one-hot encoding for categorical features, what problem can arise with high-cardinality variables?
Answer: Dimensionality explosion with many sparse binary columns
High-cardinality categorical variables create an extremely large and sparse feature matrix when one-hot encoded, increasing memory and computation costs.
In multi-class classification with a neural network, which output activation function is most commonly used?
Answer: Softmax
Softmax outputs a probability distribution over all classes (summing to 1), making it suitable for multi-class classification output layers.
What is the 'elbow method' used for in supervised learning?
Answer: Selecting the best k value for k-NN by plotting error vs. k
The elbow method plots validation error against different k values in k-NN; the optimal k is at the 'elbow' where error stops decreasing significantly.
Which technique addresses class imbalance by generating synthetic minority class samples?
Answer: SMOTE (Synthetic Minority Over-sampling Technique)
SMOTE creates synthetic samples by interpolating between existing minority class instances, helping to balance the class distribution.
What does a high AUC-ROC score (close to 1.0) indicate about a classification model?
Answer: The model perfectly separates the positive and negative classes
An AUC-ROC near 1.0 means the model ranks positive instances higher than negative ones across all thresholds, indicating excellent discriminative ability.
How this page was made: written with AI tools and published by Nathan Beja, founder of Practice Test Geeks, who is responsible for its content. We have not yet matched this exam's facts to an official source, so confirm fees, dates and rules with the exam body. Not reviewed by a subject-matter expert. Editorial policy