Overfitting in Polynomial Regression can be visualized by a graph where the polynomial curve fits even the _________ in the training data.
- accuracy
- linearity
- noise
- stability
A graph showing overfitting in Polynomial Regression will exhibit the polynomial curve fitting even the noise in the training data, not just the underlying trend.
In a case where you have a dataset with numerous outliers, which clustering algorithm would you choose and why?
- DBSCAN due to robustness to outliers
- DBSCAN due to sensitivity to noise
- K-Means due to robustness to noise
- K-Means due to sensitivity to outliers
DBSCAN (Density-Based Spatial Clustering of Applications with Noise) would be suitable since it's robust to outliers. It can identify dense clusters and leave outliers as unclassified, making it effective in such scenarios.
How can you detect whether a model is overfitting or underfitting the data?
- By analyzing the training and validation errors
- By increasing model complexity
- By looking at the model's visualizations
- By reducing model complexity
Detecting overfitting or underfitting can be done "by analyzing the training and validation errors." Overfitting shows high training accuracy but low validation accuracy, while underfitting shows poor performance on both.
You're given a dataset with several features, some of which are highly correlated. How would you handle this using dimensionality reduction techniques?
- Applying K-Means Clustering
- Applying L1 Regularization
- Applying Principal Component Analysis (PCA)
- Applying Random Forest
Principal Component Analysis (PCA) would be used to handle high correlation among features. It reduces dimensionality by creating new uncorrelated variables that capture the variance present in the original features.
The addition of _________ in the loss function is a common technique to regularize the model and prevent overfitting.
- bias
- learning rate
- regularization terms
- weights
Regularization terms (like L1 or L2 penalties) in the loss function constrain the model, reducing the risk of overfitting by preventing large weights.
You have trained an SVM but the decision boundary is not fitting well to the data. How could adjusting the hyperplane parameters help?
- Change the kernel's color
- Increase the size of the hyperplane
- Modify the regularization parameter 'C'
- Reduce the number of support vectors
Adjusting the regularization parameter 'C' controls the trade-off between margin maximization and error minimization, helping to fit the decision boundary better.
Discuss the difference between Euclidean distance and Manhattan distance metrics in the context of KNN.
- Euclidean is faster, Manhattan is more accurate
- Euclidean is for 3D, Manhattan for 2D
- Euclidean is for continuous data, Manhattan for categorical
- Euclidean uses squares, Manhattan uses absolutes
Euclidean distance is the square root of the sum of squared differences, while Manhattan distance is the sum of the absolute differences.
A dataset with very high between-class variance but low within-class variance is given. How would the LDA approach be beneficial here?
- LDA would be the same as PCA
- LDA would perform optimally due to the variance characteristics
- LDA would perform poorly
- LDA would require transformation of the dataset
LDA would "perform optimally" in this scenario, as high between-class variance and low within-class variance align perfectly with its objective of maximizing between-class variance and minimizing within-class variance.
What is the risk of using the same data for both training and testing in a Machine Learning model?
- Increase in accuracy; Reduction in bias
- Increase in complexity; Reduction in training time
- Reduction in training time; Increase in bias
- Risk of overfitting; Unrealistic performance estimates
Using the same data for training and testing leads to the risk of overfitting and provides unrealistic performance estimates. The model will have seen all the data during training, so it might not generalize well to new, unseen instances.
Can you explain the complete linkage method in Hierarchical Clustering?
- Using maximum distance between any two points in clusters
- Using mean distance between all pairs in clusters
- Using minimum distance between any two points in clusters
- Using total distance between all points in clusters
The complete linkage method in Hierarchical Clustering uses the maximum distance between any two points in the clusters to determine the linkage. It ensures that clusters are as compact as possible by focusing on the farthest points, which can sometimes lead to chain-like clusters.