When interpreting a dendrogram in Hierarchical Clustering, the height of the _________ represents the distance at which clusters are merged.
- Branches
- Leaves
- Lines
- Nodes
In a dendrogram, the height of the branches represents the distance at which clusters are merged. The higher the branch, the greater the distance, indicating that the clusters being merged are less similar. This information can guide the selection of the number of clusters and provides insights into the underlying structure of the data.
A business stakeholder wants to use a very high-degree Polynomial Regression for forecasting, arguing that it fits the historical data perfectly. How would you explain the risks of this approach and suggest a more robust method?
- Encourage the high-degree approach
- Explain the risk of overfitting and suggest using cross-validation or regularization
- Focus only on training data
- Ignore the stakeholder's suggestion
The high-degree approach is prone to overfitting and may not generalize well to future data. Explaining this risk and suggesting more robust methods such as cross-validation or regularization can help in building a more reliable forecasting model.
You are using Bootstrapping to estimate the confidence interval for a model parameter. Explain how the process works.
- By calculating the mean and standard deviation without resampling
- By randomly selecting without replacement from the dataset
- By resampling with replacement and calculating empirical quantiles of the distribution
- By splitting the data into training and validation sets
Bootstrapping to estimate the confidence interval for a model parameter involves resampling with replacement from the original data, calculating the parameter for each resampled dataset, and then determining empirical quantiles of the parameter's distribution. It allows the estimation of confidence intervals even when the underlying distribution is unknown.
What are the key differences between Hierarchical Clustering and K-Means Clustering?
- Algorithm Complexity
- Cluster Number & Structure
- Data Type
- Learning Type
Hierarchical Clustering builds a tree-like structure and does not require a predefined number of clusters, whereas K-Means requires the number of clusters in advance and builds non-hierarchical clusters.
A colleague has built a Polynomial Regression model and suspects overfitting. What diagnostic tools and techniques would you recommend to confirm or deny this suspicion?
- Cross-validation and visual inspection of residuals
- Ignore the suspicion
- Increase polynomial degree
- Look at training data only
Cross-validation and visual inspection of residuals are common techniques to detect overfitting. They can help in assessing how well the model generalizes to new data, revealing any overfitting issues.
Your classification model's accuracy is high, but precision and recall are not balanced. How would you approach this problem to get a better trade-off?
- Change the classification threshold; consider using the F1 Score
- Ignore precision and recall
- Only focus on accuracy
- Use a different dataset
Adjusting the classification threshold and considering metrics like the F1 Score, which balances precision and recall, can help achieve a more balanced trade-off between these metrics, leading to a more robust model evaluation.
In what scenarios might a custom distance metric be needed in KNN, and how would you go about implementing it?
- When K is very large
- When data has specific characteristics
- When data is uniform
- When using standardized data
A custom distance metric might be needed when data has specific characteristics that require a particular measure of similarity. Implementation involves defining a function that captures these characteristics.
The term ___________ is used to describe a model that performs well on the training data but poorly on the unseen data.
- Bootstrap
- Cross-validation
- Overfitting
- Underfitting
Overfitting refers to a situation where a model is trained too well on the training data and performs poorly on unseen data because it has learned the noise and specific patterns in the training data, rather than the underlying trend.
Which regularization technique adds L1 penalty, causing some coefficients to be exactly zero?
- Elastic Net
- Lasso
- Ridge
- nan
Lasso regularization adds an L1 penalty, causing some of the coefficients to become exactly zero, effectively removing those features from the model.
To prevent overfitting in Polynomial Regression, you might use techniques like _______, ________, or ________ regularization.
- Lasso, Accuracy, Elastic Net
- Lasso, Ridge, Elastic Net
- Lasso, Ridge, Stability
- Ridge, Stability, Elastic Net
Lasso, Ridge, and Elastic Net regularization techniques can be used to prevent overfitting in Polynomial Regression by adding constraints to the coefficients.