FAQ on Machine Learning

The two paradigms of ensemble methods are Sequential ensemble methods Parallel ensemble methods The two paradigms of ensemble methods in machine learning are: Bagging (Bootstrap Aggregating): Bagging involves training multiple instances of the same base learning algorithm on different subsets of the training data. Each subset is typically generated by sampling with replacement (bootstrap sampling) … Read more

Ensemble learning is used when you build component classifiers that are more accurate and independent from each other. The correct answer to the question “When to use ensemble learning?” would be: Ensemble learning is particularly useful in situations where you have multiple base models or algorithms that perform moderately well on their own but may … Read more

Ensemble learning is used to improve the classification, prediction, function approximation etc of a model. The correct answer to why ensemble learning is used in machine learning can be summarized in several key points: Improved Accuracy: Ensemble methods combine multiple models to produce better predictive performance than any individual model. By aggregating predictions from multiple … Read more

To solve a particular computational program, multiple models such as classifiers or experts are strategically generated and combined. This process is known as ensemble learning. Ensemble learning is a machine learning technique where multiple models, often of the same or different types, are combined to improve the overall predictive performance. The main idea behind ensemble … Read more

Combining binary classifiers Modifying binary to incorporate multiclass learning The two classification methods that Support Vector Machine (SVM) can handle are: Binary Classification: SVM is primarily used for binary classification tasks, where the goal is to classify data points into one of two categories. Multi-class Classification: SVM can also be extended to handle multi-class classification … Read more