In this Machine Learning online course, we discuss supervised standalone models’ shortcomings and learn a few techniques, such as Ensemble techniques to overcome these shortcomings.
Decision Tree is a Supervised Machine Learning algorithm used for both classification and regression problems. It is a hierarchical structure where internal nodes indicate the dataset features, branches represent the decision rules, and each leaf node indicates the result.
Random Forest is a popular supervised learning algorithm in machine learning. As the name indicates, it comprises several decision trees on the provided dataset’s several subsets. Then, it calculates the average for enhancing the dataset’s predictive accuracy.
Bagging, also known as Bootstrap Aggregation, is a meta-algorithm in machine learning used for enhancing the stability and accuracy of machine learning algorithms, which are used in statistical classification and regression.
As the name suggests, Boosting is a meta-algorithm in machine learning that converts robust classifiers from several weak classifiers. Boosting can be further classified as Gradient boosting and ADA boosting or Adaptive boosting.