A company wants to segment its customers based on their purchasing behavior. They have a fair idea that there are around 5 distinct segments but want to confirm this. Which clustering algorithm might they start with?
- K-Means Clustering
- Agglomerative Hierarchical Clustering
- Mean-Shift Clustering
- Spectral Clustering
The company might start with K-Means Clustering to confirm their idea of five distinct segments. K-Means is often used for partitioning data into a pre-specified number of clusters and can be a good choice when you have a rough idea of the number of clusters.
Variational autoencoders (VAEs) introduce a probabilistic spin to autoencoders by associating a ________ with the encoded representations.
- Probability Distribution
- Singular Value Decomposition
- Principal Component
- Regression Function
VAEs introduce a probabilistic element to autoencoders by associating a probability distribution (typically Gaussian) with the encoded representations. This allows for generating new data points.
Which regression technique is primarily used for predicting a continuous outcome variable (like house price)?
- Decision Tree Regression
- Linear Regression
- Logistic Regression
- Polynomial Regression
Linear Regression is the most common technique for predicting a continuous outcome variable, such as house prices. It establishes a linear relationship between input features and the output.
The Actor-Critic model combines value-based and ________ methods to optimize its decision-making process.
- Policy-Based
- Model-Free
- Model-Based
- Q-Learning
The Actor-Critic model combines value-based (critic) and model-free (actor) methods to optimize decision-making. The critic evaluates actions using value functions, and the actor selects actions based on this evaluation, thus combining two approaches for improved learning.
For text classification problems, the ________ variant of Naive Bayes is often used.
- K-Means
- Multinomial
- Random Forest
- SVM
In text classification, the Multinomial variant of Naive Bayes is commonly used due to its suitability for modeling discrete data like word counts.
For a non-linearly separable dataset, which property of SVMs allows them to classify the data?
- Feature selection
- Kernel functions
- Large training dataset
- Parallel processing
SVMs can classify non-linearly separable data using kernel functions, which map the data into a higher-dimensional space where it becomes linearly separable.
Which algorithm is a popular choice for solving the multi-armed bandit problem when the number of arms is large and some structure can be assumed on the rewards?
- Epsilon-Greedy
- UCB1
- Thompson Sampling
- Greedy
UCB1 (Upper Confidence Bound 1) is a popular choice for the multi-armed bandit problem when you can assume some structure on the rewards and the number of arms is large. UCB1 balances exploration and exploitation effectively by using confidence bounds to select arms.
In ________ learning, the algorithm isn't provided with the correct answers but discovers them through exploration and exploitation.
- Reinforcement
- Semi-supervised
- Supervised
- Unsupervised
Reinforcement learning involves exploration and exploitation strategies, where the algorithm learns by trial and error and discovers correct answers over time. It doesn't start with pre-defined correct answers.
In the context of text classification, Naive Bayes often works well because it can handle what type of data?
- High-Dimensional and Sparse Data
- Images and Videos
- Low-Dimensional and Dense Data
- Numeric Data
Naive Bayes is effective with high-dimensional and sparse data as it assumes independence between features, making it suitable for text data with numerous attributes.
In CNNs, the ________ layer is responsible for detecting features in an image.
- Convolutional
- Pooling
- Fully Connected
- Input
The 'Convolutional' layer in a Convolutional Neural Network (CNN) is responsible for detecting features in an image using convolution operations.