Is DBSCAN sensitive to the choice of Epsilon and MinPts? Why or why not?
- No, they are auto-calculated parameters
- No, they have minimal effect on the outcome
- Yes, they define the shape of the clusters
- Yes, they influence the density of clusters
DBSCAN is indeed sensitive to the choice of Epsilon and MinPts. These parameters are crucial in determining the density of the clusters, as Epsilon controls the maximum radius of the neighborhood, and MinPts sets the minimum number of points required to form a dense region. Selecting inappropriate values can lead to suboptimal clustering results.
The term _________ refers to a situation where a regression model fits the training data too closely, resulting in poor performance on new data.
- Bias
- Overfitting
- Regularization
- Underfitting
Overfitting refers to a situation where a regression model fits the training data too closely, capturing noise and resulting in poor performance on unseen data.
What is the broad field of study that encompasses Machine Learning, Deep Learning, and other computational techniques to enable intelligent decision-making?
- Artificial Intelligence
- Computational Science
- Data Mining
- Deep Learning
Artificial Intelligence (AI) is the broad field that includes Machine Learning, Deep Learning, and other techniques aimed at creating intelligent systems.
What are the potential challenges in determining the optimal values for Epsilon and MinPts in DBSCAN?
- Difficulty in selecting values that balance density and granularity of clusters
- Lack of robustness to noise
- Limited flexibility in shapes
- Risk of overfitting the data
Determining optimal values for Epsilon and MinPts in DBSCAN is challenging as it requires a careful balance between the density and granularity of clusters. Too large Epsilon can merge clusters, while too small can create many tiny clusters. Selecting MinPts affects the required density, making this tuning a complex task.
The __________ function in Logistic Regression models the log odds of the probability of the dependent event.
- Linear
- Logit
- Polynomial
- Sigmoid
The Logit function in Logistic Regression models the log odds of the probability of the dependent event occurring.
How are rewards and penalties used to guide the learning process in reinforcement learning?
- To group data based on similarities
- To guide the agent's actions
- To label the data
- To reduce complexity
In reinforcement learning, rewards and penalties guide the agent's actions, encouraging beneficial behaviors and discouraging detrimental ones.
How do hyperplanes differ in hard-margin SVM and soft-margin SVM?
- Color difference
- Difference in dimensionality
- Difference in size
- Flexibility in handling misclassifications
Hard-margin SVM does not allow any misclassifications, while soft-margin SVM provides flexibility in handling misclassifications.
You're working with a dataset that has clusters of various shapes and densities. Which clustering algorithm would be best suited for this, and why?
- DBSCAN
- Hierarchical Clustering
- K-Means
- Mean Shift
DBSCAN is best suited for clusters of various shapes and densities, as it's a density-based clustering method and doesn't rely on spherical assumptions about the data.
How is the amount of variance explained calculated in PCA?
- By dividing each eigenvalue by the sum of all eigenvalues
- By multiplying the eigenvalues with the mean
- By summing all eigenvalues
- By taking the square root of the eigenvalues
The amount of variance explained by each principal component in PCA is calculated by dividing the corresponding eigenvalue by the sum of all eigenvalues, and often expressed as a percentage.
You are asked to apply Hierarchical Clustering to a dataset with mixed types of data (categorical and numerical). What challenges could arise and how would you tackle them?
- All of the above
- Computationally intensive clustering
- Difficulty in defining a suitable distance metric
- Inaccurate clustering due to the scale of numerical features
The primary challenge in clustering mixed types of data is defining a suitable distance metric that can handle both categorical and numerical features. You may need to standardize numerical features and find appropriate ways to measure distances for categorical attributes (e.g., using Gower distance). This choice will significantly influence the quality and interpretability of the clustering.
You are having difficulty interpreting the coefficients of your Logistic Regression model. How might the Logit function and Odds Ratio help in understanding them?
- By transforming coefficients into R-squared values
- By transforming coefficients into log-odds and allowing interpretation in terms of odds
- By transforming coefficients into odds
- By transforming coefficients into probabilities
The Logit function and Odds Ratio can help in understanding the coefficients by transforming them into log-odds and allowing interpretation in terms of the change in odds for a one-unit change in the predictor.
Describe a situation where a high Accuracy might be misleading, and a different metric (e.g., Precision, Recall, or F1-Score) might be more appropriate.
- When the dataset has equal classes, Precision is more appropriate
- When the dataset has only one class, Recall is more appropriate
- When the dataset is imbalanced, other metrics like Precision or Recall may be more informative
- nan
In imbalanced datasets, where one class significantly outnumbers the other, Accuracy can be misleading. Even a naive model predicting the majority class will have high Accuracy. Metrics like Precision, Recall, or F1-Score provide more insight into the model's performance on the minority class.