How does DBSCAN handle noise in the data, and what distinguishes it from other clustering methods?
- Classifies Noise as a Separate Cluster
- Considers Noise in Cluster Formation
- Handles Noise Through Density-Based Clustering
- Ignores Noise
DBSCAN handles noise by classifying it as a separate category and distinguishes itself by utilizing a density-based approach that groups together points that are closely packed, considering the rest as noise.
In the context of regression analysis, ________ accounts for the number of predictors in the model and adjusts the R-Squared accordingly.
- Adjusted R-Squared
- MAE
- R-Squared
- RMSE
Adjusted R-Squared accounts for the number of predictors in the model, providing a more balanced view of model fit by penalizing the inclusion of irrelevant features. It adjusts the R-Squared value based on the number of predictors, making it more suitable for comparing models with different numbers of variables.
What is a binary classification problem?
- A problem with two algorithms
- A problem with two classes or outcomes
- A problem with two input variables
- A problem with two solutions
A binary classification problem involves predicting one of two possible classes or outcomes. It's a specific type of classification where the target variable has only two possible values.
What are the potential issues with using a large value of K in KNN, and how can they be mitigated?
- All of the above
- Complexity, can be mitigated by feature selection
- Overfitting, can be mitigated by reducing dimensions
- Underfitting, can be mitigated by reducing K
A large value of K in KNN may cause underfitting. It can be mitigated by reducing K or using techniques like cross-validation.
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.
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 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.