At RetainKit, we aim to tackle the challenging problem of churn at SaaS companies by using AI and machine learning. If you run a SaaS company and you have churn issues, we'd be happy to talk to you and see if our product could help. You can also follow us on Product Hunt Upcoming. In the early days of Post Planner (my previous startup), everything was going fine, except that it wasn't. We had built a product that solved a problem, or so we thought.

Deploying models trained in your research environment is not always a simple task. Your research environment, your production programming language, and the interplay between them may affect the ease of introducing new statistical models in production. In this blog post, I'll demonstrate the complete flow from training a Random Forest model in R, exporting it to a PMML file and finally scoring by the model in production oriented languages using Scoruby and Goscore. PMML stands for Predictive Model Markup Language and can represent models from research environments as XML files which can be later loaded and run in production. Scoruby and Goscore are code packages written by myself, that consume PMML files of various models and execute them in Go and Ruby under production memory and speed constraints.

In machine learning, there's something called the "No Free Lunch" theorem. In a nutshell, it states that no one algorithm works best for every problem, and it's especially relevant for supervised learning (i.e. For example, you can't say that neural networks are always better than decision trees or vice-versa. There are many factors at play, such as the size and structure of your dataset. As a result, you should try many different algorithms for your problem, while using a hold-out "test set" of data to evaluate performance and select the winner.

Both neural networks and decision trees are popular machine learning methods and are widely used to solve problems from diverse domains. These two classifiers are commonly used base classifiers in an ensemble framework. In this paper, we first present a new variant of oblique decision tree based on a linear classifier, then construct an ensemble classifier based on the fusion of a fast neural network, random vector functional link network and oblique decision trees. Random Vector Functional Link Network has an elegant closed form solution with extremely short training time. The neural network partitions each training bag (obtained using bagging) at the root level into C subsets where C is the number of classes in the dataset and subsequently, C oblique decision trees are trained on such partitions. The proposed method provides a rich insight into the data by grouping the confusing or hard to classify samples for each class and thus, provides an opportunity to employ fine-grained classification rule over the data. The performance of the ensemble classifier is evaluated on several multi-class datasets where it demonstrates a superior performance compared to other state-of- the-art classifiers.

To do this you should have access to the tree structure of the random forest, as you are with classifier if you find the "gain" associated through the path of the variables (leaf) then you can calculate the contribution for each leaf. I cannot help more with Random forest (I am more verse with boosting).

"A field of study that gives computers the ability to learn without being explicitly programmed" Before you scream'Skynet' and run give machine learning the credit it's due. Machine learning has altered the world invisibly and mostly for the better: Optimizing traffic flows, matching suitor on dating sites to figuring out whether your cell are cancerous or just benign are just some of the more visible outcomes of the research and application underway in the field. This infographic covers it all: It's humble origins, how it works, what approaches and models are being used and some of the most popular applications for the field. It misses one of course: Gamification (after all, why post this if it isn't related to what we do!). At IamProgrez we mainly do research and develop applications that fall into the Decision Tree Learning and Artificial Neural Network's categories. It's a field in the past year that has become increasingly important to us as we drill deeper into out gamified skills analysis, particularly in the predictive modelling our gamification systems as it applies to our users.

The infinitesimal jackknife (IJ) has recently been applied to the random forest to estimate its prediction variance. These theorems were verified under a traditional random forest framework which uses classification and regression trees (CART) and bootstrap resampling. However, random forests using conditional inference (CI) trees and subsampling have been found to be not prone to variable selection bias. Here, we conduct simulation experiments using a novel approach to explore the applicability of the IJ to random forests using variations on the resampling method and base learner. Test data points were simulated and each trained using random forest on one hundred simulated training data sets using different combinations of resampling and base learners. Using CI trees instead of traditional CART trees as well as using subsampling instead of bootstrap sampling resulted in a much more accurate estimation of prediction variance when using the IJ. The random forest variations here have been incorporated into an open source software package for the R programming language.

Decision Trees are commonly used in data mining with the objective of creating a model that predicts the value of a target (or dependent variable) based on the values of several input (or independent variables). In today's post, we discuss the CART decision tree methodology. The CART or Classification & Regression Trees methodology was introduced in 1984 by Leo Breiman, Jerome Friedman, Richard Olshen and Charles Stone as an umbrella term to refer to the following types of decision trees: Classification Trees: where the target variable is categorical and the tree is used to identify the "class" within which a target variable would likely fall into. Regression Trees: where the target variable is continuous and tree is used to predict it's value. The CART algorithm is structured as a sequence of questions, the answers to which determine what the next question, if any should be.

Decision trees algorithms use a gain function to select the best split during the tree's induction. This function is crucial to obtain trees with high predictive accuracy. Some gain functions can suffer from a bias when it compares splits of different arities. Quinlan proposed a gain ratio in C4.5's information gain function to fix this bias. In this paper, we present an updated version of the gain ratio that performs better as it tries to fix the gain ratio's bias for unbalanced trees and some splits with low predictive interest.