Regularization techniques help in preventing overfitting. Which of these is NOT a regularization technique: Batch Normalization, Dropout, Adam Optimizer, L1 Regularization?
- Adam Optimizer
- Batch Normalization
- Dropout
- L1 Regularization
Adam Optimizer is not a regularization technique. It's an optimization algorithm used in training neural networks, while the others are regularization methods.
A medical research team is studying the relationship between various health metrics (like blood pressure, cholesterol level) and the likelihood of developing a certain disease. The outcome is binary (disease: yes/no). Which regression model should they employ?
- Decision Tree Regression
- Linear Regression
- Logistic Regression
- Polynomial Regression
Logistic Regression is the appropriate choice for binary outcomes, such as the likelihood of developing a disease (yes/no). It models the probability of a binary outcome based on predictor variables, making it well-suited for this medical research.
In the context of the multi-armed bandit problem, what is regret?
- The feeling of loss and remorse
- An optimization metric
- A random variable
- An arm selection policy
In the context of the multi-armed bandit problem, regret is an optimization metric that quantifies how much an agent's total reward falls short of the best possible reward it could have achieved by always choosing the best arm. It's a way to measure how well an agent's arm selection policy performs.
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.
In the context of reinforcement learning, what does the term "gradient" in "Policy Gradient Methods" refer to?
- The direction of steepest ascent in the policy space
- A mathematical term used to describe the rate of change
- The probability distribution of actions
- The value function
In "Policy Gradient Methods," the "gradient" refers to the direction in the policy space that increases the expected reward. It guides policy updates to maximize reward.
Which regression method assumes a linear relationship between the independent and dependent variables?
- Decision Tree Regression
- Logistic Regression
- Polynomial Regression
- Ridge Regression
Polynomial Regression assumes a linear relationship between the independent and dependent variables. It models relationships as polynomial functions, and other regression methods may assume different relationships.