One common regularization technique involves adding a penalty to the loss function based on the magnitude of the coefficients, known as ________ regularization.
- L1 (Lasso)
- L2 (Ridge)
- Elastic Net
- Mean Squared Error
L2 (Ridge) regularization adds a penalty based on the sum of squared coefficients, helping to control the model's complexity and reduce overfitting.
Support Vector Machines (SVM) aim to find a ______ that best divides a dataset into classes.
- Cluster
- Decision Boundary
- Hyperplane
- Mean
Support Vector Machines aim to find a hyperplane that best divides a dataset into classes. This hyperplane maximizes the margin between the classes, making it a powerful tool for binary classification tasks. The concept of the "support vector" is crucial in SVM.
In Gaussian Mixture Models, the "mixture" refers to the combination of ________ Gaussian distributions.
- Different
- Similar
- Identical
- Overlapping
In a Gaussian Mixture Model (GMM), the "mixture" implies that we combine multiple Gaussian (normal) distributions to model complex data distributions. The term "identical" indicates that these component Gaussians are the same type.
The weights and biases in a neural network are adjusted during the ________ process to minimize the loss.
- Forward Propagation
- Backpropagation
- Initialization
- Regularization
Weights and biases in a neural network are adjusted during the 'Backpropagation' process to minimize the loss by propagating the error backward through the network.
In the context of deep learning, what is the primary use case of autoencoders?
- Image Classification
- Anomaly Detection
- Text Generation
- Reinforcement Learning
The primary use case of autoencoders in deep learning is for anomaly detection. They can learn the normal patterns in data and detect anomalies or deviations from these patterns, making them useful in various applications, including fraud detection and fault diagnosis.
When models are too simple and cannot capture the underlying trend of the data, it's termed as ________.
- Misfitting
- Overfitting
- Simplification
- Underfitting
When a model is too simple to capture the underlying patterns in the data, it is referred to as "underfitting." Underfit models have high bias and low variance, making them ineffective for predictions.
A bioinformatics researcher is trying to visualize the similarities and differences between different genes in a 2D space. The data is high dimensional. Which technique would provide a good visualization emphasizing local similarities?
- t-Distributed Stochastic Neighbor Embedding (t-SNE)
- Principal Component Analysis
- Linear Regression
- A* Search Algorithm
t-SNE is well-suited for visualizing high-dimensional data by preserving local similarities. It maps data points to a 2D space in a way that emphasizes neighborhood relationships, making it ideal for visualizing gene similarities in high-dimensional data.
What is the primary goal of exploration in reinforcement learning?
- To gather information about the environment
- To maximize immediate rewards
- To stick with known actions
- To build a policy
Exploration's primary goal is to gather information about the environment, helping an RL agent learn and make better decisions in the long run.
In the context of healthcare, what is the significance of machine learning models being interpretable?
- To provide insights into the model's decision-making process and enable trust in medical applications
- To speed up the model training process
- To make models run on low-end hardware
- To reduce the amount of data required
Interpretable models are essential in healthcare to ensure that the decisions made by the model are understandable and can be trusted, which is crucial for patient safety and regulatory compliance.
In the context of regression analysis, what does the slope of a regression line represent?
- Change in the dependent variable
- Change in the independent variable
- Intercept of the line
- Strength of the relationship
The slope of a regression line represents the change in the dependent variable for a one-unit change in the independent variable. It quantifies the impact of the independent variable on the dependent variable.