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Ensemble Machine Learning With Python (7-Day Mini-Course)

#artificialintelligence

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.


Ensemble Learning to Improve Machine Learning Results

#artificialintelligence

Ensemble learning helps improve machine learning results by combining several models. This approach allows the production of better predictive performance compared to a single model. That is why ensemble methods placed first in many prestigious machine learning competitions, such as the Netflix Competition, KDD 2009, and Kaggle. The Statsbot team wanted to give you the advantage of this approach and asked a data scientist, Vadim Smolyakov, to dive into three basic ensemble learning techniques. Ensemble methods are meta-algorithms that combine several machine learning techniques into one predictive model in order to decrease variance (bagging), bias (boosting), or improve predictions (stacking).


Ensemble Learning to Improve Machine Learning Results

#artificialintelligence

Ensemble methods are meta-algorithms that combine several machine learning techniques into one predictive model in order to decrease variance (bagging), bias (boosting), or improve predictions (stacking). Ensemble learning helps improve machine learning results by combining several models. This approach allows the production of better predictive performance compared to a single model. That is why ensemble methods placed first in many prestigious machine learning competitions, such as the Netflix Competition, KDD 2009, and Kaggle. The Statsbot team wanted to give you the advantage of this approach and asked a data scientist, Vadim Smolyakov, to dive into three basic ensemble learning techniques.


How to Develop a Random Subspace Ensemble With Python

#artificialintelligence

Random Subspace Ensemble is a machine learning algorithm that combines the predictions from multiple decision trees trained on different subsets of columns in the training dataset. Randomly varying the columns used to train each contributing member of the ensemble has the effect of introducing diversity into the ensemble and, in turn, can lift performance over using a single decision tree. It is related to other ensembles of decision trees such as bootstrap aggregation (bagging) that creates trees using different samples of rows from the training dataset, and random forest that combines ideas from bagging and the random subspace ensemble. Although decision trees are often used, the general random subspace method can be used with any machine learning model whose performance varies meaningfully with the choice of input features. In this tutorial, you will discover how to develop random subspace ensembles for classification and regression.


Want to Win Competitions? Pay Attention to Your Ensembles.

@machinelearnbot

Summary: Want to win a Kaggle competition or at least get a respectable place on the leaderboard? These days it's all about ensembles and for a lot of practitioners that means reaching for random forests. Random forests have indeed been very successful but it's worth remembering that there are three different categories of ensembles and some important hyper parameters tuning issues within each Here's a brief review. The Kaggle competitions are like formula racing for data science. Winners edge out competitors at the fourth decimal place and like Formula 1 race cars, not many of us would mistake them for daily drivers.