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.

A collection of minimal and clean implementations of machine learning algorithms. This project is targeting people who want to learn internals of ml algorithms or implement them from scratch. The code is much easier to follow than the optimized libraries and easier to play with. All algorithms are implemented in Python, using numpy, scipy and autograd.

A collection of minimal and clean implementations of machine learning algorithms. This project is targeting people who wants to learn internals of ml algorithms or implement them from scratch. The code is much easier to follow than the optimized libraries and easier to play with. All algorithms are implemented in Python, using numpy, scipy and autograd.

A collection of minimal and clean implementations of machine learning algorithms. This project is targeting people who wants to learn internals of ml algorithms or implement them from scratch. The code is much easier to follow than the optimized libraries and easier to play with. All algorithms are implemented in Python, using numpy, scipy and autograd.

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.

I recently read an article in which the winner of a Kaggle Competition was not shy about sharing his technique for winning not one, but several of the analytical competitions. "I always use Gradient Boosting," he said. And then added, "but the key is Feature Engineering." A couple days later, a friend who read the same article called and asked, "What is this Feature Engineering that he's talking about?" It was a timely question, as I was in the process of developing a risk model for a client, and specifically, I was working through the stage of Feature Engineering.

Choosing features to improve a performance of a particular algorithm is a difficult question. Currently here is PCA, which is hard to understand (although it can be used out-of-the-box), is not easy to interpret and requires centralizing and scaling of features. In addition, it does not allow to improve prediction performance for a particular outcome (if its accuracy is lower than for others or it has a particular importance). My method enables to use features without preprocessing. Therefore a resulting prediction is easy to explain.

Algorithm selection can be challenging for machine learning newcomers. Often when building classifiers, especially for beginners, an approach is adopted to problem solving which considers single instances of single algorithms. However, in a given scenario, it may prove more useful to chain or group classifiers together, using the techniques of voting, weighting, and combination to pursue the most accurate classifier possible. Ensemble learners are classifiers which provide this functionality in a variety of ways. This post will provide an overview of bagging, boosting, and stacking, arguably the most used and well-known of the basic ensemble methods.

One point to consider is are you interested in making predictions or understanding associations and carrying out inference (confidence intervals around effects). Although random forests provide a variable-importance summary, this technique is primarily aimed at prediction; there is no inference. Many researchers think they are interested in making predictions, but often there is a mismatch with their goals. With that said, you can make predictions with glm and gamlss. You also have the flexibility of regression.

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. The amount of time devoted and the sometimes extreme techniques wouldn't be appropriate in a data science production environment, but like paddle shifters and exotic suspensions, some of those improvement find their way into day-to-day life. Ensembles, or teams of predictive models working together, have been the core strategy for winning at Kaggle. They've been around for a long time but they are getting better.