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Which algorithm is used for solving the ‘Knapsack Problem’?
A. Dynamic Programming
B. Greedy Algorithm
C. Backtracking
D. Branch and Bound
Answer: Dynamic Programming
What is ‘bias-variance tradeoff’ in machine learning?
A. The balance between a model's complexity and its ability to generalize
B. The tradeoff between training and testing data sizes
C. The tradeoff between different types of features
D. The tradeoff between different algorithms
Answer: The balance between a model's complexity and its ability to generalize
Which of the following is an example of a clustering algorithm?
A. K-means Clustering
B. Logistic Regression
C. Support Vector Machine (SVM)
D. Decision Tree
Answer: K-means Clustering
Which of the following is a hyperparameter in a machine learning model?
A. Learning Rate
B. Accuracy
C. F1 Score
D. Confusion Matrix
Answer: Learning Rate
What does ‘cross-validation’ help with in machine learning?
A. Estimating the performance of the model on unseen data
B. Selecting the features for the model
C. Optimizing the model's hyperparameters
D. Reducing the size of the dataset
Answer: Estimating the performance of the model on unseen data
In the context of machine learning, what is ‘gradient descent’?
A. An optimization algorithm used to minimize the loss function
B. A method for data normalization
C. A technique for feature selection
D. An algorithm for clustering data
Answer: An optimization algorithm used to minimize the loss function
Which algorithm is used for dimensionality reduction and visualization of high-dimensional data?
A. t-Distributed Stochastic Neighbor Embedding (t-SNE)
B. Linear Discriminant Analysis (LDA)
C. K-means Clustering
D. Support Vector Machine (SVM)
Answer: t-Distributed Stochastic Neighbor Embedding (t-SNE)
What is the main advantage of using a Random Forest over a single decision tree?
A. It reduces overfitting by averaging multiple decision trees
B. It requires less computational power
C. It performs better with unstructured data
D. It simplifies the model
Answer: It reduces overfitting by averaging multiple decision trees
Which technique is used to handle missing values in a dataset?
A. Imputation
B. Normalization
C. Feature Engineering
D. Data Augmentation
Answer: Imputation