Ensemble Techniques and Unsupervised Learning

What are model combination schemes in ensemble learning?

• Feature selection algorithms

• Regression analysis techniques

• Model combination schemes include averaging, weighted averaging, voting, and stacking.

• Data normalization methods

Explain the concept of voting mechanisms in ensemble methods.

• Weighted voting is the only method used in ensemble techniques.

• Voting mechanisms only apply to classification tasks.

• Ensemble methods rely solely on a single model's prediction.

• Voting mechanisms in ensemble methods combine predictions from multiple models to improve accuracy, using majority or weighted voting.

What is bagging and how does it improve model performance?

• Bagging is an ensemble method that improves model performance by reducing variance through training multiple models on bootstrapped subsets of data.

• Bagging is a technique that focuses solely on reducing bias in models.

• Bagging increases model performance by using the entire dataset without sampling.

• Bagging is a method that only uses a single model to improve accuracy.

Describe the boosting method and its advantages over bagging.

• Boosting reduces bias by averaging predictions from multiple models.

• Bagging improves accuracy by combining weak models sequentially.

• Boosting improves model accuracy by sequentially correcting errors of weak learners, while bagging reduces variance by averaging independent models.

• Boosting uses a single strong learner to make predictions.

What is the stacking approach in ensemble learning?

• Stacking is a method of feature selection in machine learning.

• Stacking combines models by averaging their predictions without a meta-learner.

• Stacking involves using a single model to make predictions.

• The stacking approach combines multiple models using a meta-learner to improve prediction accuracy.

How does K-means clustering work in unsupervised learning?

• K-means clustering groups data based on the average value of each feature.

• K-means clustering is a supervised learning algorithm that requires labeled data.

• K-means clustering is an unsupervised learning algorithm that partitions data into 'k' clusters by minimizing the distance between data points and their respective cluster centroids.

• K-means clustering uses a decision tree to classify data points into clusters.

What is the role of KNN in instance-based learning?

• KNN uses a decision tree to classify instances.

• KNN classifies instances based on the majority class of their nearest neighbors.

• KNN requires labeled data for training before classification.

• KNN predicts instances based on a linear regression model.

Explain Gaussian mixture models and their applications.

• Gaussian mixture models are used for clustering, density estimation, and anomaly detection.

• Gaussian mixture models are a type of decision tree algorithm.

• Gaussian mixture models are primarily for time series forecasting.

• Gaussian mixture models are only used for linear regression.

What is the expectation maximization algorithm?

• A method for calculating the mean of a dataset.

• A technique for visualizing high-dimensional data.

• The Expectation Maximization algorithm is a method for finding maximum likelihood estimates in models with latent variables.

• An algorithm for sorting data in ascending order.

How do ensemble techniques enhance the accuracy of machine learning models?

• Ensemble techniques work by selecting the best single model for predictions.

• Ensemble techniques only focus on increasing model complexity.

• Ensemble techniques reduce the amount of data needed for training.

• Ensemble techniques enhance accuracy by combining multiple models to reduce variance and bias, leading to more robust predictions.