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Originally published on Towards AI the World's Leading AI and Technology News and Media Company. If you are building an AI-related product or service, we invite you to consider becoming an AI sponsor. At Towards AI, we help scale AI and technology startups. Let us help you unleash your technology to the masses. Life is full of decisions and eventually, we do measure which option to take on some logical-based analysis.
Ensemble learning refers to machine learning models that combine the predictions from two or more models. Ensembles are an advanced approach to machine learning that are often used when the capability and skill of the predictions are more important than using a simple and understandable model. As such, they are often used by top and winning participants in machine learning competitions like the One Million Dollar Netflix Prize and Kaggle Competitions. Modern machine learning libraries like scikit-learn Python provide a suite of advanced ensemble learning methods that are easy to configure and use correctly without data leakage, a common concern when using ensemble algorithms. In this crash course, you will discover how you can get started and confidently bring ensemble learning algorithms to your predictive modeling project with Python in seven days.
Bagging is an ensemble algorithm that fits multiple models on different subsets of a training dataset, then combines the predictions from all models. Random forest is an extension of bagging that also randomly selects subsets of features used in each data sample. Both bagging and random forests have proven effective on a wide range of different predictive modeling problems. Although effective, they are not suited to classification problems with a skewed class distribution. Nevertheless, many modifications to the algorithms have been proposed that adapt their behavior and make them better suited to a severe class imbalance. In this tutorial, you will discover how to use bagging and random forest for imbalanced classification.
Decision trees can suffer from high variance which makes their results fragile to the specific training data used. Building multiple models from samples of your training data, called bagging, can reduce this variance, but the trees are highly correlated. Random Forest is an extension of bagging that in addition to building trees based on multiple samples of your training data, it also constrains the features that can be used to build the trees, forcing trees to be different. This, in turn, can give a lift in performance. In this tutorial, you will discover how to implement the Random Forest algorithm from scratch in Python.