Which method can be seen as a probabilistic extension to k-means clustering, allowing soft assignments of data points?
- Mean-Shift Clustering
- Hierarchical Clustering
- Expectation-Maximization (EM)
- DBSCAN Clustering
The Expectation-Maximization (EM) method is a probabilistic extension to k-means, allowing soft assignments of data points based on probability distributions.
Which method involves reducing the number of input variables when developing a predictive model?
- Dimensionality Reduction
- Feature Expansion
- Feature Scaling
- Model Training
Dimensionality reduction is the process of reducing the number of input variables by selecting the most informative ones, combining them, or transforming them into a lower-dimensional space. This helps simplify models and can improve their efficiency and performance.
With the aid of machine learning, wearable devices can predict potential health events by analyzing ________ data.
- Sensor
- Biometric
- Personal
- Lifestyle
Machine learning applied to wearable devices can predict potential health events by analyzing biometric data. This includes information such as heart rate, blood pressure, and other physiological indicators that provide insights into the wearer's health status.
A medical imaging company is trying to diagnose diseases from X-ray images. Considering the spatial structure and patterns in these images, which type of neural network would be most appropriate?
- Convolutional Neural Network (CNN)
- Recurrent Neural Network (RNN)
- Feedforward Neural Network
- Radial Basis Function Network
A Convolutional Neural Network (CNN) is designed to capture spatial patterns and structures in images effectively, making it suitable for image analysis, such as X-ray diagnosis.
ICA is often used to separate ________ that have been mixed into a single data source.
- Signals
- Components
- Patterns
- Features
Independent Component Analysis (ICA) is used to separate mixed components in a data source, making 'Components' the correct answer.
How does the Actor-Critic model differ from traditional Q-learning in reinforcement learning?
- In Actor-Critic, the Actor and Critic are separate entities.
- Q-learning uses value iteration, while Actor-Critic uses policy iteration.
- Actor-Critic relies on neural networks, while Q-learning uses decision trees.
- In Q-learning, the Critic updates the policy.
The Actor-Critic model is different from traditional Q-learning as it separates the task of policy learning (Actor) from value estimation (Critic), whereas in Q-learning, these functions are often combined. This separation allows for more flexibility and efficiency in learning policies in complex environments.
Why is ethics important in machine learning applications?
- To ensure fairness and avoid bias
- To improve model accuracy
- To speed up model training
- To reduce computational cost
Ethics in machine learning is vital to ensure fairness and avoid bias, preventing discrimination against certain groups or individuals in model predictions. It's a fundamental concern in the field of AI and ML.
A bank wants to use transaction details to determine the likelihood that a transaction is fraudulent. The outcome is either "fraudulent" or "not fraudulent." Which regression method would be ideal for this purpose?
- Decision Tree Regression
- Linear Regression
- Logistic Regression
- Polynomial Regression
Logistic Regression is the ideal choice for binary classification tasks, like fraud detection (fraudulent or not fraudulent). It models the probability of an event occurring, making it the right tool for this scenario.
How does the Random Forest algorithm handle the issue of overfitting seen in individual decision trees?
- By aggregating predictions from multiple trees
- By increasing the tree depth
- By reducing the number of features
- By using a smaller number of trees
Random Forest handles overfitting by aggregating predictions from multiple decision trees. This ensemble method combines the results from different trees, reducing the impact of individual overfitting.
In the context of transfer learning, what is the main advantage of using pre-trained models on large datasets like ImageNet?
- Feature Extraction
- Faster Training
- Reduced Generalization
- Lower Computational Cost
The main advantage of using pre-trained models on large datasets is "Feature Extraction." Pre-trained models have learned useful features, which can be transferred to new tasks, saving time and data.