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 Trees

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 Forests

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

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.

Boosting

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.