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.
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.
To avoid overfitting in large neural networks, one might employ a technique known as ________, which involves dropping out random neurons during training.
- Batch Normalization
- L2 Regularization
- Gradient Descent
- Dropout
The 'Dropout' technique involves randomly deactivating a fraction of neurons during training, which helps prevent overfitting in large neural networks.
If you're working with high-dimensional data and you want to reduce its dimensionality for visualization without necessarily preserving the global structure, which method would be apt?
- Principal Component Analysis (PCA)
- Linear Discriminant Analysis (LDA)
- t-Distributed Stochastic Neighbor Embedding (t-SNE)
- Independent Component Analysis (ICA)
When you want to reduce high-dimensional data for visualization without preserving global structure, t-SNE is apt. It focuses on local similarities, making it effective for revealing clusters and patterns in the data, even if the global structure is not preserved.
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.