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.
How would you handle a situation in which the SVM is performing poorly due to the choice of kernel?
- Change the dataset
- Change to a more appropriate kernel using cross-validation
- Ignore the issue
- Use only linear kernel
Changing to an appropriate kernel using cross-validation can enhance the performance if the current kernel is not suitable for the data.
In DBSCAN, what does the term 'Epsilon' refer to?
- Edge Distance
- Error Rate
- Estimated Density
- Maximum Radius of the Neighborhood
In DBSCAN, 'Epsilon' refers to the maximum radius of the neighborhood around a data point. If there are enough points within this radius (defined by MinPts), the point is considered a core point, leading to the formation of a cluster. It's a critical parameter affecting the clustering result, controlling how close points must be to form a cluster.
You've detected a high Variance Inflation Factor (VIF) for one of the variables in your Multiple Linear Regression model. What does this indicate, and how would you proceed?
- High multicollinearity and consider removing or combining variables
- Low multicollinearity
- No multicollinearity
- The variable is not significant
A high VIF indicates high multicollinearity, meaning the variable is highly correlated with other variables in the model. You may consider removing or combining variables, applying regularization, or using dimensionality reduction techniques to address this issue and improve the model's performance.
What is the primary purpose of using Cross-Validation in Machine Learning?
- To enhance the model's complexity
- To estimate the model's performance on unseen data
- To increase the training speed
- To select optimal hyperparameters
Cross-Validation's primary purpose is to estimate the model's performance on unseen data by dividing the dataset into training and validation sets. It provides a more reliable evaluation than using a single static validation set.
You're building a recommendation system without access to labeled data. How would you proceed using unsupervised learning techniques?
- Combining labeled and unlabeled data
- Employing labeled data
- Using clustering methods
- Using reinforcement strategies
Clustering methods are a common approach in Unsupervised Learning to group data based on similarities, suitable for recommendation systems without labeled data.
A company wants to predict customer churn based on historical data. What considerations must be made in selecting and tuning a Machine Learning model for this task?
- Considering the business context, available data, model interpretability, and performance metrics
- Focusing only on accuracy
- Ignoring feature engineering
- Selecting the most complex model available
Predicting customer churn requires understanding the business context, the nature of the data, and the need for model interpretability. Metrics such as precision, recall, and F1-score might be more relevant than mere accuracy.
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.
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'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.