In the context of SVM, the support vectors are the data points that lie closest to the _________.
- Data center
- Hyperplane
- Kernel
- Margin
Support vectors are the data points that lie closest to the hyperplane and influence its position and orientation.
Linear Discriminant Analysis (LDA) is often used for dimensionality reduction before applying a classification algorithm, as it seeks to find the axis that best separates the ___________.
- classes
- data
- features
- variables
LDA seeks to find the axis that "best separates the classes" to reduce dimensionality while retaining class separation.
A company wants to classify its products into different categories based on various features. How could LDA be applied here, considering both within-class and between-class variances?
- Apply LDA to balance within-class and between-class variances for effective classification
- Focus on within-class variance and ignore between-class variance
- Ignore within-class variance and focus on between-class variance
- Use another method
LDA could be applied by considering both within-class and between-class variances, seeking to "balance within-class and between-class variances for effective classification." This ensures that products in the same category are similar, while products in different categories are distinct.
How does the K-Means clustering algorithm determine the centroids?
- Based on Density
- By Class Labels
- Minimizing Euclidean Distance
- Random Selection
K-Means clustering algorithm determines the centroids by iteratively minimizing the sum of squared Euclidean distances between the data points and the centroids of their respective clusters.
Can you explain what separates support vectors from other data points in SVM?
- Cluster membership
- Color
- Distance to hyperplane
- Size
Support vectors are the data points closest to the hyperplane, and they determine its position in SVM.
How do interpretability and explainability vary between AI, Machine Learning, and Deep Learning?
- AI and Deep Learning are equally interpretable, Machine Learning is least
- AI is least interpretable, Machine Learning and Deep Learning are equally interpretable
- AI is most interpretable, Machine Learning is moderate, Deep Learning is least
- Machine Learning is most interpretable, AI is moderate, Deep Learning is least
Generally, AI techniques can vary in interpretability, traditional Machine Learning models tend to be more interpretable, and Deep Learning models are often the least interpretable due to their complexity.
How does the choice of loss function affect the learning process in a Machine Learning model?
- It defines the optimization algorithm
- It determines the learning rate
- It measures how well the model's predictions match the true values
- It selects the type of regularization
The loss function measures the discrepancy between the predicted values and the actual values, guiding the optimization process. Different loss functions can emphasize different aspects of the error, influencing how the model learns.
How do the hyperparameters in Ridge and Lasso affect the bias-variance tradeoff?
- Increase bias, reduce variance
- Increase both bias and variance
- No effect
- Reduce bias, increase variance
The hyperparameters in Ridge and Lasso control the regularization strength. Increasing them increases bias but reduces variance, helping to prevent overfitting.
How does the average linkage method differ from single and complete linkage in Hierarchical Clustering?
- Uses the mean of all distances between pairs in clusters
- Uses the median of all distances between pairs in clusters
- Uses the mode of all distances between pairs in clusters
- Uses the total of all distances between pairs in clusters
The average linkage method calculates the mean of all pairwise distances between the points in the clusters to determine the linkage. Single linkage uses the minimum distance, while complete linkage uses the maximum distance. Average linkage typically results in more balanced clusters, as it considers the overall distribution of distances.
How do multi-class classification problems differ from binary classification problems?
- Multi-class has more features
- Multi-class has multiple classes; binary has two
- Multi-class has two classes; binary has multiple
- No difference
Binary classification deals with two classes, while multi-class classification deals with more than two. Multi-class problems can be more complex and require different handling or algorithms compared to binary classification.