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In machine learning, what does ‘bias’ refer to?
A. The error introduced by approximating a real-world problem with a simplified model
B. The error due to random noise in the data
C. The variance of the model predictions
D. The number of features in the dataset
Answer: The error introduced by approximating a real-world problem with a simplified model
Which technique is used for dimensionality reduction?
A. Principal Component Analysis (PCA)
B. K-means Clustering
C. Naive Bayes
D. Linear Regression
Answer: Principal Component Analysis (PCA)
What does ‘overfitting’ refer to in machine learning?
A. A model that performs well on training data but poorly on unseen data
B. A model that performs poorly on both training and test data
C. A model that is too simple to capture the underlying patterns
D. A model that takes too long to train
Answer: A model that performs well on training data but poorly on unseen data
What is the primary goal of machine learning?
A. To make machines capable of learning from data
B. To develop hardware for computers
C. To design user interfaces
D. To create new programming languages
Answer: To make machines capable of learning from data
What is the ‘curse of dimensionality’?
A. The problem where the performance of a model degrades as the number of features increases
B. The issue of overfitting with too few features
C. The challenge of scaling features to a common range
D. The difficulty in handling missing values
Answer: The problem where the performance of a model degrades as the number of features increases
Which of the following is a common technique for ‘dimensionality reduction’?
A. Principal Component Analysis (PCA)
B. K-means Clustering
C. Support Vector Machine (SVM)
D. Naive Bayes
Answer: Principal Component Analysis (PCA)
What is the purpose of ‘ensemble learning’?
A. To combine predictions from multiple models to improve overall performance
B. To select the most relevant features from the dataset
C. To reduce the dimensionality of the data
D. To handle missing values in the dataset
Answer: To combine predictions from multiple models to improve overall performance
What does ‘bagging’ stand for in ensemble methods?
A. Bootstrap Aggregating
B. Binary Aggregating
C. Batch Aggregating
D. Bayesian Aggregating
Answer: Bootstrap Aggregating
Which technique is used to ‘handle categorical variables’?
A. One-Hot Encoding
B. Dimensionality Reduction
C. Feature Scaling
D. Clustering
Answer: One-Hot Encoding