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.
What are the potential drawbacks of using PCA for dimensionality reduction?
- It always improves model performance
- It can lead to information loss and doesn't consider class labels
- It normalizes the variance of the data
- It removes all noise and outliers
The potential drawbacks of using PCA include the risk of information loss since it only considers variance, not class labels, and might remove meaningful information that doesn't align with the directions of maximum variance.
In a real-world customer segmentation problem, how might you apply clustering to optimize marketing strategies?
- All of the Above
- By Clustering Based on Behavior
- By Clustering Based on Geography
- By Clustering Based on Product Preference
Clustering can be applied in various ways to optimize marketing strategies, including grouping customers based on product preference, geography, behavior, or a combination of these factors.
Explain the Bias-Variance tradeoff in the context of Cross-Validation.
- Increasing k decreases bias but may increase variance
- Increasing k decreases both bias and variance
- Increasing k increases bias but decreases variance
- Increasing k increases both bias and variance
The Bias-Variance tradeoff in the context of k-fold Cross-Validation refers to the balance between bias (error due to overly simplistic assumptions) and variance (error due to excessive complexity). Increasing k generally decreases bias since more data is used for training, but it may lead to an increase in variance as the validation set becomes more similar to the training set.
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.
Clustering is a common task in __________ learning, where data is grouped based on inherent similarities without the use of labels.
- reinforcement
- semi-supervised
- supervised
- unsupervised
Unsupervised learning commonly involves clustering, where data is grouped based on similarities without using labels.
What is the primary difference between the Gini Index and entropy when used in Decision Trees?
- Calculation Method
- Complexity
- Scale
- Units
Gini Index and entropy are both used to measure purity, but they are calculated differently. Entropy uses logarithms, while Gini Index does not.
When a Decision Tree is too complex and fits the training data too well, __________ techniques can be applied to simplify the model.
- Bagging
- Boosting
- Normalizing
- Pruning
When a Decision Tree is overfitting (too complex), pruning techniques can be applied to simplify the model. Pruning involves removing branches that have little predictive power, thereby reducing the complexity and the risk of overfitting.
What's the difference between simple linear regression and multiple linear regression?
- One is for classification, the other for regression
- One is linear, the other is nonlinear
- One uses one predictor, the other uses more than one
- One uses time series data, the other doesn't
Simple linear regression involves one independent variable to predict the dependent variable, while multiple linear regression uses two or more independent variables for prediction.
What is the main goal of Artificial Intelligence as a scientific discipline?
- To create complex algorithms
- To improve data storage
- To make money through technology
- To mimic human intelligence and decision-making
The primary goal of Artificial Intelligence is to develop systems that can mimic human intelligence and decision-making processes.