In SVM, what does the term "kernel" refer to?
- A feature transformation
- A hardware component
- A software component
- A support vector
The term "kernel" in Support Vector Machines (SVM) refers to a feature transformation. Kernels are used to map data into a higher-dimensional space, making it easier to find a linear hyperplane that separates different classes.
In the bias-variance decomposition of the expected test error, which component represents the error due to the noise in the training data?
- Bias
- Both Bias and Variance
- Neither Bias nor Variance
- Variance
In the bias-variance trade-off, the component that represents the error due to noise in the training data is both bias and variance. Bias refers to the error introduced by overly simplistic assumptions in the model, while variance represents the error due to model sensitivity to fluctuations in the training data. Together, they account for the expected test error.
What is the primary goal of the Principal Component Analysis (PCA) technique in machine learning?
- Clustering Data
- Finding Anomalies
- Increasing Dimensionality
- Reducing Dimensionality
PCA's primary goal is to reduce dimensionality by identifying and retaining the most significant features, making data analysis and modeling more efficient.
To prevent a model from becoming too complex and overfitting the training data, ________ techniques are often applied.
- Regularization
- Optimization
- Stochastic Gradient Descent
- Batch Normalization
Regularization techniques add penalties to the loss function to discourage complex models, helping prevent overfitting and improving model generalization.
Game-playing agents, like those used in board games or video games, often use ________ learning to optimize their strategies.
- Reinforcement
- Semi-supervised
- Supervised
- Unsupervised
Game-playing agents frequently employ reinforcement learning. This approach involves learning by trial and error, where agents receive feedback (rewards) based on their actions, helping them optimize their strategies over time.
In a fraud detection system, you have data with numerous features. You suspect that not all features are relevant, and some may even be redundant. Before feeding the data into a classifier, you want to reduce its dimensionality without losing critical information. Which technique would be apt for this?
- Principal Component Analysis (PCA)
- Support Vector Machines (SVM)
- Breadth-First Search
- Quick Sort
Principal Component Analysis (PCA) is used for dimensionality reduction. It identifies the most significant features in the data, allowing you to reduce dimensionality while retaining critical information. In a fraud detection system, this is valuable for improving model performance.
How is NLP primarily used in healthcare?
- Identifying Medical Trends
- Patient Entertainment
- Managing Hospital Inventory
- Extracting Medical Information
NLP is primarily used in healthcare to extract structured information from unstructured medical notes, aiding in decision-making and research.
What is the main purpose of regularization techniques like dropout and L2 regularization in deep learning models?
- Reduce overfitting
- Increase model complexity
- Speed up training
- Improve convergence
Regularization techniques like dropout and L2 regularization are used to reduce overfitting by adding penalties for complex models and preventing overfitting of training data.
________ regression is best suited for binary classification problems.
- Lasso
- Linear
- Logistic
- Polynomial
Logistic regression is a type of regression used in binary classification problems, where the outcome variable has two possible classes (e.g., yes/no, true/false, 0/1). It models the probability of one of the classes.
A key challenge in machine learning ethics is ensuring that algorithms do not perpetuate or amplify existing ________.
- Inequalities
- Biases
- Advantages
- Opportunities
Ensuring that algorithms do not perpetuate or amplify existing inequalities is a fundamental challenge in machine learning ethics. Addressing this challenge requires creating more equitable models and datasets.