Which regularization method would you likely use if you suspect some of the features are entirely irrelevant?
- Elastic Net
- Lasso
- Ridge
- nan
Lasso regularization is useful when some features are suspected to be irrelevant, as it can set the coefficients for those features to zero, effectively removing them.
In the context of Machine Learning, what is the term used for the values that the model is trying to predict?
- Clusters
- Features
- Labels
- Predictors
The term "Labels" is used to describe the values that the model is trying to predict. These are the known outcomes or targets that the model is trained to predict.
The Eigenvalue corresponding to each principal component in PCA determines the ________ of that component.
- direction
- magnitude
- normalization
- scaling
In PCA, the Eigenvalue corresponding to a principal component determines its "magnitude," representing the amount of variance that component explains in the original data. A higher Eigenvalue indicates more significant variance explained by that component.
A hospital wants to utilize Machine Learning to predict patient readmission rates. How would you approach this problem?
- Clustering, Image Recognition
- Fraud Detection, Supply Chain Management
- Supervised Learning, Predictive Modeling
- Weather Prediction, Text Classification
Utilizing Supervised Learning algorithms and Predictive Modeling, such as logistic regression or gradient boosting, would help in predicting patient readmission rates by analyzing historical patient data and relevant features.
What role do centroids play in the K-Means clustering algorithm?
- They are the midpoint of clusters
- They define the classes of data
- They determine the number of clusters
- They determine the shape of clusters
In K-Means, centroids are the midpoints of clusters. The algorithm assigns each data point to the nearest centroid, and the centroids are recalculated until convergence.
You're tasked with improving the recommendation system of an e-commerce platform. What Machine Learning techniques would you consider implementing?
- Collaborative Filtering, Content-Based Filtering
- Image Recognition, Text Classification
- Supply Chain Optimization, Clustering
- Weather Prediction, Fraud Detection
Collaborative Filtering and Content-Based Filtering are widely used in recommendation systems. Collaborative Filtering uses user-item interactions, while Content-Based Filtering uses item attributes to make personalized recommendations.
How would you interpret a complex dendrogram to define clusters in a dataset?
- By analyzing the color and shape of branches
- By analyzing the height and intersections of branches
- By analyzing the length and width of branches
- By analyzing the orientation and angles of branches
A dendrogram is interpreted by analyzing the height and intersections of branches. The height where branches merge indicates the distance between clusters, and cutting the dendrogram at a specific height allows you to define clusters. The intersections represent the linkage between clusters, which aids in understanding the hierarchical structure.
In DBSCAN, if a point has more than MinPts within its Epsilon neighborhood, it's considered a _________ point.
- border point
- cluster
- core point
- noise point
In DBSCAN, a core point is a point that has at least MinPts within its Epsilon neighborhood. Core points are considered central to a cluster, and other points within the Epsilon distance of a core point may also be part of the same cluster.
Why might you choose to use Polynomial Regression in a model?
- To fit a straight line
- To model non-linear relationships
- To predict binary outcomes
- To reduce the number of features
Polynomial Regression is used to model non-linear relationships between the dependent and independent variables. It adds complexity to the model by fitting polynomial terms, allowing for a better fit when linear models are inadequate.
You are working with a small dataset, and your model is prone to overfitting. What techniques could you employ to mitigate this issue?
- Add complexity
- Reduce complexity
- Use L1 regularization
- Use cross-validation and data augmentation
Using techniques like cross-validation and data augmentation can mitigate overfitting when working with a small dataset. Cross-validation ensures that the model is evaluated on unseen data, and data augmentation artificially increases the size of the dataset, reducing the risk of overfitting.