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.
You are developing a recommendation system for a music app. While the system's bias is low, it tends to offer very different song recommendations for slight variations in user input. This is an indication of which issue in the bias-variance trade-off?
- High Bias
- High Variance
- Overfitting
- Underfitting
This scenario indicates overfitting in the bias-variance trade-off. Overfit models tend to provide very different recommendations for slight input changes, suggesting that the model is fitting noise in the data and not generalizing well to new user preferences.
Which process involves transforming and creating new variables to improve a machine learning model's predictive performance?
- Data preprocessing
- Feature engineering
- Hyperparameter tuning
- Model training
Feature engineering is the process of transforming and creating new variables based on the existing data to enhance a model's predictive performance. This can involve scaling, encoding, or creating new features from existing ones.
A researcher is working on a medical imaging problem with a limited amount of labeled data. To improve the performance of the deep learning model, the researcher decides to use a model pre-trained on a large generic image dataset. This approach is an example of what?
- Transfer Learning
- Reinforcement Learning
- Ensemble Learning
- Supervised Learning
Transfer learning is the practice of using a pre-trained model as a starting point to solve a new problem. In this case, it leverages prior knowledge from generic images to enhance medical image analysis.
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.
How do conditional GANs (cGANs) differ from standard GANs?
- cGANs incorporate conditional information for data generation.
- cGANs are designed exclusively for image generation.
- cGANs have no significant differences from standard GANs.
- cGANs use unsupervised learning.
cGANs differ by including additional conditional information, such as labels, to guide the data generation process, making them more versatile.
In scenarios where you want the model to discover the best action to take by interacting with an environment, you'd likely use ________ learning.
- Reinforcement
- Semi-supervised
- Supervised
- Unsupervised
Reinforcement learning is used in situations where an agent interacts with an environment, learns from its actions, and discovers the best actions through rewards and penalties.
In SVM, the data points that are closest to the decision boundary and influence its orientation are called ______.
- Decision Points
- Influence Points
- Margin Points
- Support Vectors
The data points that are closest to the decision boundary are known as "Support Vectors" in Support Vector Machines (SVM). These points play a crucial role in determining the orientation of the decision boundary.
GRUs are often considered a middle ground between basic RNNs and ________ in terms of complexity and performance.
- LSTMs
- CNNs
- Autoencoders
- K-Means Clustering
GRUs (Gated Recurrent Units) are a compromise between basic RNNs and LSTMs, offering a balance between the complexity and performance of these two types of recurrent networks.