Why is Independent Component Analysis (ICA) primarily used in applications like audio signal processing?
- It's more computationally efficient
- It separates mixed sources effectively
- It requires less data for training
- It's based on supervised learning
ICA is used in audio signal processing because it can effectively separate mixed sources, making it useful for source separation and blind signal separation tasks.
A healthcare company wants to classify patients into risk categories based on their medical history. They have a vast amount of patient data, but the relationships between variables are complex and non-linear. Which algorithm might be more suitable for this task?
- Decision Trees
- K-Nearest Neighbors (K-NN)
- Logistic Regression
- Naive Bayes
Decision Trees are suitable for complex and non-linear relationships between variables. They can capture intricate patterns in patient data, making them effective for risk categorization in healthcare.
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.
A medical research company is working on image data, where they want to classify microscopic images into cancerous and non-cancerous categories. The boundary between these categories is not linear. Which algorithm would be a strong candidate for this problem?
- Convolutional Neural Network (CNN)
- Logistic Regression
- Naive Bayes Classifier
- Principal Component Analysis
Convolutional Neural Networks (CNNs) are excellent for image classification tasks, especially when dealing with non-linear boundaries. They use convolutional layers to extract features from images, making them suitable for tasks like cancerous/non-cancerous image classification.
The term "exploitation" in reinforcement learning refers to which of the following?
- Utilizing the best-known actions
- Trying new, unexplored actions
- Maximizing exploration
- Modifying the environment
Exploitation involves utilizing the best-known actions to maximize rewards based on current knowledge, minimizing risk and uncertainty.
________ learning is often used for discovering hidden patterns in data.
- Reinforcement
- Semi-supervised
- Supervised
- Unsupervised
Unsupervised learning is a machine learning approach where algorithms are used to identify patterns in data without explicit guidance. It is commonly employed for data exploration and pattern discovery.
When dealing with high-dimensional data, which of the two algorithms (k-NN or Naive Bayes) is likely to be more efficient in terms of computational time?
- Both Equally Efficient
- Naive Bayes
- Neither is Efficient
- k-NN
Naive Bayes is typically more efficient in high-dimensional data due to its simple probabilistic calculations, while k-NN can suffer from the "curse of dimensionality."
In the k-NN algorithm, as the value of k increases, the decision boundary becomes __________.
- Linear
- More complex
- More simplified
- Non-existent
As the value of k in k-NN increases, the decision boundary becomes more simplified because it is based on fewer neighboring data points.