In the context of DBSCAN, if two core points are within the Epsilon distance of each other, they are said to be __________.
- directly dense reachable
- indirectly dense reachable
- separate
- unrelated
In DBSCAN, if two core points are within the Epsilon distance of each other, they are said to be directly dense reachable. This concept is used to establish connections between core points and helps in forming clusters by linking together points that are close to one another.
In what scenarios might DBSCAN be a less appropriate clustering algorithm compared to others?
- When clusters have different densities
- When clusters have similar densities
- When data distribution is highly skewed
- When data is uniformly distributed
DBSCAN might be less suitable when clusters have different densities, as the same Epsilon and MinPts parameters apply to all clusters. This can lead to difficulty in capturing clusters with widely varying densities, making other clustering methods that can adapt to varying density clusters potentially more appropriate in such scenarios.
Your Decision Tree is suffering from high bias. How could adjusting the parameters related to entropy or the Gini Index help in this scenario?
- Add more training data
- Increase tree complexity by fine-tuning split criteria
- Reduce tree complexity by fine-tuning split criteria
- Remove features
High bias often means the model is too simple. Adjusting the parameters related to entropy or the Gini Index to create more complex splits can help capture underlying patterns in the data, thereby reducing bias and potentially improving predictive accuracy.
In a situation with mixed types of features, a __________ distance metric might be preferable in KNN.
- Cosine
- Euclidean
- Gower
- Manhattan
The Gower distance metric can handle mixed types of features (numerical, categorical) and is often preferable in such cases.
In DBSCAN, Epsilon is the maximum radius of the neighborhood from a data point, and MinPts is the minimum number of points required to form a ________.
- border point
- cluster
- core point
- noise point
In DBSCAN, Epsilon defines the neighborhood radius, and MinPts defines the minimum number of points required to form a cluster. If a point has at least MinPts within its Epsilon neighborhood, a cluster is formed.
What is multicollinearity in the context of Multiple Linear Regression?
- Adding interaction effects
- High correlation among variables
- Lowering the bias of the model
- Reducing overfitting
Multicollinearity refers to a situation where two or more independent variables in a Multiple Linear Regression model are highly correlated with each other.
What are the main differences between PCA and Linear Discriminant Analysis (LDA) as techniques for dimensionality reduction?
- Both techniques work the same way
- PCA is a type of LDA
- PCA is unsupervised, LDA is supervised
- PCA maximizes within-class variance, LDA between
The main difference between PCA and LDA is that PCA is an unsupervised technique that maximizes the total variance in the data, while LDA is a supervised technique that maximizes the between-class variance and minimizes the within-class variance. This makes LDA more suitable when class labels are available, while PCA can be used without them.
How are financial institutions using Machine Learning to detect fraudulent activities?
- Fraud Detection
- Personalized Education
- Recommending Media
- Weather Prediction
Financial institutions use Machine Learning algorithms to detect fraudulent activities by analyzing transaction patterns and identifying anomalies or suspicious behavior.
In a scenario where the targets are imbalanced, how would this affect the training and testing process, and what strategies would you apply to handle it?
- Apply resampling techniques
- Focus on specific evaluation metrics
- Ignore the imbalance
- Use only the majority class
Imbalanced targets can bias the model towards the majority class, leading to poor performance on the minority class. Applying resampling techniques like oversampling the minority class or undersampling the majority class balances the data. This, combined with using appropriate evaluation metrics like precision, recall, or F1 score, ensures that the model is more sensitive to the minority class.
The _________ linkage method in Hierarchical Clustering minimizes the variance of the distances between clusters.
- Average Linkage
- Complete Linkage
- Single Linkage
- Ward's Method
Ward's Method minimizes the variance of the distances between clusters. It considers the sum of squared deviations from the mean and tends to create equally sized clusters. This method can be beneficial when we want compact, spherical clusters and when minimizing within-cluster variance is a primary consideration.