In a situation where the data is densely packed in some regions and sparse in others, how would the choice of K and distance metric influence the results, and what would be the best approach?
- Choose a fixed K and Euclidean distance
- Choose a large K and any distance metric
- Choose a small K and ignore distance metric
- Choose an appropriate K and distance metric, considering data distribution
Considering the data distribution and choosing an appropriate value of K and distance metric can help address the issue of varying data density in KNN.
Why is centroid initialization important in K-Means clustering?
- All of the Above
- It determines the final clusters
- It prevents overfitting
- It speeds up the convergence process
Centroid initialization is important in K-Means as it can significantly affect the final clusters. Poor initialization can lead to suboptimal clusters or slow convergence.
Can you name a popular clustering algorithm used in Machine Learning?
- Decision Trees
- K-Means
- K-Nearest Neighbors
- Linear Regression
K-Means is a widely-used clustering algorithm that partitions data into K distinct, non-overlapping clusters based on similarity.
Logistic Regression is commonly used for __________ problems where the outcome has two categories.
- Binary classification
- Clustering
- Multiclass classification
- Regression
Logistic Regression is primarily used for binary classification problems where the outcome has only two categories.
Given a scenario where the feature correlation is very high, how would you choose between Ridge, Lasso, and ElasticNet?
- It doesn't matter
- Prefer ElasticNet
- Prefer Lasso
- Prefer Ridge
ElasticNet is preferred when there's multicollinearity, as it combines L1 and L2 penalties, balancing the properties of Ridge and Lasso.
In the context of Polynomial Regression, using too low a degree may lead to _________, while too high a degree may lead to _________.
- accuracy, inaccuracy
- overfitting, underfitting
- stability, instability
- underfitting, overfitting
Using too low a degree may cause the model to be too simple and underfit the data, while too high a degree can lead to a complex model that overfits the data.
Can classification be used to predict continuous values?
- No
- Only with specific algorithms
- Sometimes
- Yes
Classification is used to predict discrete categories or classes, not continuous values. Regression techniques are used for predicting continuous values.
When using Bootstrapping for estimating the standard error of a statistic, the process involves repeatedly resampling the data ________ times.
- infinite
- k
- multiple
- n
When using Bootstrapping for estimating the standard error of a statistic, the process involves repeatedly resampling the data "n" times. The resampling is performed with replacement, and statistical measures are calculated for each bootstrap sample, providing an empirical distribution from which the standard error can be estimated.
How would you optimize the hyperparameters in an SVM to achieve the best performance on a specific dataset?
- Guess the hyperparameters
- Optimize the kernel only
- Use grid search or random search with cross-validation
- Use only the default values
Utilizing techniques like grid search or random search with cross-validation allows for systematic hyperparameter tuning to achieve the best performance.
You're clustering a large dataset, and computational efficiency is a concern. Which clustering techniques might be more suitable, and why?
- DBSCAN
- Hierarchical Clustering
- K-Means
- K-Means and DBSCAN
Both K-Means and DBSCAN offer good computational efficiency, making them suitable for handling large datasets. K-Means, in particular, can be implemented with scalable variations like Mini-Batch K-Means.