A dataset with very high between-class variance but low within-class variance is given. How would the LDA approach be beneficial here?
- LDA would be the same as PCA
- LDA would perform optimally due to the variance characteristics
- LDA would perform poorly
- LDA would require transformation of the dataset
LDA would "perform optimally" in this scenario, as high between-class variance and low within-class variance align perfectly with its objective of maximizing between-class variance and minimizing within-class variance.
What is the risk of using the same data for both training and testing in a Machine Learning model?
- Increase in accuracy; Reduction in bias
- Increase in complexity; Reduction in training time
- Reduction in training time; Increase in bias
- Risk of overfitting; Unrealistic performance estimates
Using the same data for training and testing leads to the risk of overfitting and provides unrealistic performance estimates. The model will have seen all the data during training, so it might not generalize well to new, unseen instances.
Can you explain the complete linkage method in Hierarchical Clustering?
- Using maximum distance between any two points in clusters
- Using mean distance between all pairs in clusters
- Using minimum distance between any two points in clusters
- Using total distance between all points in clusters
The complete linkage method in Hierarchical Clustering uses the maximum distance between any two points in the clusters to determine the linkage. It ensures that clusters are as compact as possible by focusing on the farthest points, which can sometimes lead to chain-like clusters.
What are the potential drawbacks of using PCA for dimensionality reduction?
- It always improves model performance
- It can lead to information loss and doesn't consider class labels
- It normalizes the variance of the data
- It removes all noise and outliers
The potential drawbacks of using PCA include the risk of information loss since it only considers variance, not class labels, and might remove meaningful information that doesn't align with the directions of maximum variance.
In a real-world customer segmentation problem, how might you apply clustering to optimize marketing strategies?
- All of the Above
- By Clustering Based on Behavior
- By Clustering Based on Geography
- By Clustering Based on Product Preference
Clustering can be applied in various ways to optimize marketing strategies, including grouping customers based on product preference, geography, behavior, or a combination of these factors.
How can Cross-Validation help in hyperparameter tuning?
- By allowing repeated testing on the same validation set
- By improving model accuracy directly
- By providing robust performance estimates to select the best hyperparameters
- By reducing computation time
Cross-Validation enables hyperparameter tuning by providing a robust estimate of the model's performance across different data splits. This process helps to find hyperparameters that generalize well to unseen data, minimizing the risk of overfitting, and allowing a more informed selection of optimal hyperparameters.
How do Ridge and Lasso regularization techniques interact with Polynomial Regression to mitigate overfitting?
- By adding a penalty term to constrain coefficients
- By fitting low-degree polynomials
- By ignoring interaction terms
- By increasing the model's complexity
Ridge and Lasso regularization techniques mitigate overfitting in Polynomial Regression by adding a penalty term to the loss function. This constrains the coefficients, reducing the complexity of the model, and helps in avoiding overfitting.
You need to build a recommendation system with a clear explanation of why certain recommendations are made. How would you approach this considering AI, Machine Learning, and Deep Learning?
- AI, for its rule-based logic
- Deep Learning, for its data-driven insights
- Machine Learning, for its predictive analysis
- nan
Traditional AI models, often based on rule-based logic, may provide clear explanations for recommendations, whereas Machine Learning and Deep Learning models may be less interpretable.
Can you explain how the choice of kernel in SVM affects the decision boundary?
- Changes the color
- Increases computation time
- Reduces data size
- Transforms the feature space
Different kernels transform the feature space in different ways, affecting the shape and position of the decision boundary in SVM.
Clustering is a common task in __________ learning, where data is grouped based on inherent similarities without the use of labels.
- reinforcement
- semi-supervised
- supervised
- unsupervised
Unsupervised learning commonly involves clustering, where data is grouped based on similarities without using labels.