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What is boosting in ensemble learning?
Boosting is an ensemble learning technique that combines multiple weak learners sequentially, with each learner focusing on the examples that the previous ones misclassified.
Boosting is an ensemble learning technique that combines multiple weak learners sequentially, with each learner focusing on the examples that the previous ones misclassified.
See lessWhat is bagging in ensemble learning?
Bagging (Bootstrap Aggregating) is an ensemble learning technique that combines multiple models trained on bootstrap samples of the data and averages their predictions.
Bagging (Bootstrap Aggregating) is an ensemble learning technique that combines multiple models trained on bootstrap samples of the data and averages their predictions.
See lessWhat are some examples of ensemble learning techniques?
Examples of ensemble learning techniques include bagging, boosting, and random forests.
Examples of ensemble learning techniques include bagging, boosting, and random forests.
See lessWhat is ensemble learning in machine learning?
Ensemble learning combines multiple models to improve prediction accuracy and robustness compared to individual models.
Ensemble learning combines multiple models to improve prediction accuracy and robustness compared to individual models.
See lessWhat is bias-variance tradeoff in machine learning?
The bias-variance tradeoff refers to the balance between the bias (error due to overly simplistic models) and variance (error due to overly complex models) of a machine learning algorithm.
The bias-variance tradeoff refers to the balance between the bias (error due to overly simplistic models) and variance (error due to overly complex models) of a machine learning algorithm.
See lessWhat is regularization in machine learning?
Regularization is a technique used to prevent overfitting by adding a penalty term to the loss function, discouraging overly complex models.
Regularization is a technique used to prevent overfitting by adding a penalty term to the loss function, discouraging overly complex models.
See lessWhat is cross-validation in machine learning?
Cross-validation is a technique used to evaluate the performance of a machine learning model by splitting the data into multiple subsets for training and testing.
Cross-validation is a technique used to evaluate the performance of a machine learning model by splitting the data into multiple subsets for training and testing.
See lessHow can overfitting be prevented in machine learning?
Overfitting can be prevented by using techniques such as cross-validation, regularization, and early stopping.
Overfitting can be prevented by using techniques such as cross-validation, regularization, and early stopping.
See lessWhat is overfitting in machine learning?
Overfitting occurs when a model learns to memorize the training data instead of generalizing to unseen data, resulting in poor performance on new data.
Overfitting occurs when a model learns to memorize the training data instead of generalizing to unseen data, resulting in poor performance on new data.
See lessWhat is the role of a loss function in machine learning?
A loss function measures the difference between predicted and actual values, guiding the learning process towards better model performance.
A loss function measures the difference between predicted and actual values, guiding the learning process towards better model performance.
See less