Data Science & Analytics – 30 Flashcards
Flashcard 1
Q: What is supervised learning?
A: A type of machine learning that uses labeled data to predict
outcomes (e.g., classification, regression).
Flashcard 2
Q: What is unsupervised learning?
A: Learning patterns from unlabeled data (e.g., clustering, PCA).
Flashcard 3
Q: What is overfitting?
A: When a model learns noise in training data; low training
error, high testing error.
Flashcard 4
Q: How do you reduce overfitting?
A: Regularization, dropout, cross-validation, more data,
simplified model.
Flashcard 5
Q: What is feature scaling?
, A: Normalizing numerical data so all features have similar range;
required for KNN, SVM, K-means, Gradient Descent.
Flashcard 6
Q: Classification vs Regression?
A: Classification predicts categories; regression predicts numeric
values.
Flashcard 7
Q: What is a confusion matrix?
A: A table showing TP, FP, TN, FN to evaluate classification.
Flashcard 8
Q: Formula for precision?
A: TP / (TP + FP)
Flashcard 9
Q: Formula for recall?
A: TP / (TP + FN)
Flashcard 10
Q: What is F1-score?
A: Harmonic mean of precision and recall.
Flashcard 1
Q: What is supervised learning?
A: A type of machine learning that uses labeled data to predict
outcomes (e.g., classification, regression).
Flashcard 2
Q: What is unsupervised learning?
A: Learning patterns from unlabeled data (e.g., clustering, PCA).
Flashcard 3
Q: What is overfitting?
A: When a model learns noise in training data; low training
error, high testing error.
Flashcard 4
Q: How do you reduce overfitting?
A: Regularization, dropout, cross-validation, more data,
simplified model.
Flashcard 5
Q: What is feature scaling?
, A: Normalizing numerical data so all features have similar range;
required for KNN, SVM, K-means, Gradient Descent.
Flashcard 6
Q: Classification vs Regression?
A: Classification predicts categories; regression predicts numeric
values.
Flashcard 7
Q: What is a confusion matrix?
A: A table showing TP, FP, TN, FN to evaluate classification.
Flashcard 8
Q: Formula for precision?
A: TP / (TP + FP)
Flashcard 9
Q: Formula for recall?
A: TP / (TP + FN)
Flashcard 10
Q: What is F1-score?
A: Harmonic mean of precision and recall.
