How can you evaluate the performance of an LDA model?
- By checking the size of the scatter matrices
- By comparing with PCA
- Using confusion matrix and ROC curves
- Using only accuracy
The performance of an LDA model can be evaluated using metrics like the "confusion matrix and ROC curves." These tools provide insights into the model's ability to classify instances correctly and its trade-off between sensitivity and specificity.
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 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 does DBSCAN handle outliers compared to other clustering algorithms?
- Considers them as part of existing clusters
- Ignores them completely
- Treats more isolated points as noise
- Treats them as individual clusters
DBSCAN has a unique way of handling outliers, treating more isolated points as noise rather than forcing them into existing clusters or forming new clusters. This approach allows DBSCAN to identify clusters of varying shapes and sizes while ignoring sparse or irrelevant points, making it more robust to noise and outliers compared to some other clustering methods.
What could be the potential problems if the assumptions of Simple Linear Regression are not met?
- Model May Become Biased or Inefficient
- Model May Overfit
- Model Will Always Fail
- No Impact on Model
If the assumptions of Simple Linear Regression are not met, the model may become biased or inefficient, leading to unreliable estimates. It may also affect the validity of statistical tests.