Predicting and improving player retention is crucial to the success of mobile Free-to-Play games. This paper explores the problem of rapid retention prediction in this context. Heuristic modeling approaches are introduced as a way of building simple rules for predicting short-term retention. Compared to common classification algorithms, our heuristic-based approach achieves reasonable and comparable performance using information from the first session, day, and week of player activity.

Top Machine Learning & Data Mining Books - in this post, we have scraped various signals (e.g. We have combined all signals to compute the Quality Score for each book and publish the list of top Machine Learning and Data Mining books. The readers will love the list because it is data-driven & objective. This book is very well rated on Amazon website and is written by three professors from USC, Stanford and University of Washington. The book's authors: Gareth James, Daniela Witten, Trevor Hastie, & Rob Tibshirani all have backgrounds in statistics.

In three months (as of June 2016) the New Orleans Saints will play a football game against the Atlanta Falcons. I want to know who will win. I ask my friend and he says the Saints. Technically this is a predictive model, but it's probably not worth much. I can improve upon this model by asking other people who they think will win.

We propose a novel methodology, forest floor, to visualize and interpret random forest (RF) models. RF is a popular and useful tool for non-linear multi-variate classification and regression, which yields a good trade-off between robustness (low variance) and adaptiveness (low bias). Direct interpretation of a RF model is difficult, as the explicit ensemble model of hundreds of deep trees is complex. Nonetheless, it is possible to visualize a RF model fit by its mapping from feature space to prediction space. Hereby the user is first presented with the overall geometrical shape of the model structure, and when needed one can zoom in on local details. Dimensional reduction by projection is used to visualize high dimensional shapes. The traditional method to visualize RF model structure, partial dependence plots, achieve this by averaging multiple parallel projections. We suggest to first use feature contributions, a method to decompose trees by splitting features, and then subsequently perform projections. The advantages of forest floor over partial dependence plots is that interactions are not masked by averaging. As a consequence, it is possible to locate interactions, which are not visualized in a given projection. Furthermore, we introduce: a goodness-of-visualization measure, use of colour gradients to identify interactions and an out-of-bag cross validated variant of feature contributions.

Instructions for how to install the neccessary software for this tutorial is available here. Data for the tutorial can be downloaded by running ./data/get-data.sh (requires wget). Certain algorithms don't scale well when there are millions of features. For example, decision trees require computing some sort of metric (to determine the splits) on all the feature values (or some fraction of the values as in Random Forest and Stochastic GBM). Therefore, computation time is linear in the number of features. Algorithms can deal with data sparsity (where many of the feature values are zero) in different ways.

Lately in the tech world, everything has been revolving around artificial intelligence and machine learning. If you've been interested in learning more about machine learning and getting in on it as well, now you can. Thanks to this fantastic bundle that we have available on the Android Headlines Store. This bundle features 10 courses, over 400 lessons and is discounted by 94% right now. Included in the bundle is Quant Trading Using Machine Learning, Learn by Example: Statistics and Data Science in R, Learn by Example: Hadoop & MapReduce for Big Data Problems, Byte Size Chunks: Java Object-Oriented Programming & Design, An Introduction to Machine Learning & NLP in Python, Byte-Sized-Chunks: Twitter Sentiment Analysis (In Python), Byte-Sized-Chunks: Decision Trees and Random Forests, An Introduction to Deep Learning & Computer Vision, Byte-Sized-Chunks: Recommendation Systems, and From 0 to 1: Learn Python Programming – Easy as Pie.

Today, I'm going to explain in plain English the top 10 most influential data mining algorithms as voted on by 3 separate panels in this survey paper. Once you know what they are, how they work, what they do and where you can find them, my hope is you'll have this blog post as a springboard to learn even more about data mining. In order to do this, C4.5 is given a set of data representing things that are already classified. A classifier is a tool in data mining that takes a bunch of data representing things we want to classify and attempts to predict which class the new data belongs to. Sure, suppose a dataset contains a bunch of patients.

This paper deals with prediction of anopheles number, the main vector of malaria risk, using environmental and climate variables. The variables selection is based on an automatic machine learning method using regression trees, and random forests combined with stratified two levels cross validation. The minimum threshold of variables importance is accessed using the quadratic distance of variables importance while the optimal subset of selected variables is used to perform predictions. Finally the results revealed to be qualitatively better, at the selection, the prediction , and the CPU time point of view than those obtained by GLM-Lasso method.

I'm implementing a machine learning structure to try and predict fraud on financial systems like banks, etc... This means that there is a lot of different data that can be used to train the model eg. I'm having trouble deciding which structure is the best for this problem. I have some experience with decision trees but currently I have started to question if a neural network would be better for this kind of problem. Also if any other method would be best please feel free to enlighten me.

Process mining techniques aim to extract insights in processes from event logs. One of the challenges in process mining is identifying interesting and meaningful event labels that contribute to a better understanding of the process. Our application area is mining data from smart homes for elderly, where the ultimate goal is to signal deviations from usual behavior and provide timely recommendations in order to extend the period of independent living. Extracting individual process models showing user behavior is an important instrument in achieving this goal. However, the interpretation of sensor data at an appropriate abstraction level is not straightforward. For example, a motion sensor in a bedroom can be triggered by tossing and turning in bed or by getting up. We try to derive the actual activity depending on the context (time, previous events, etc.). In this paper we introduce the notion of label refinements, which links more abstract event descriptions with their more refined counterparts. We present a statistical evaluation method to determine the usefulness of a label refinement for a given event log from a process perspective. Based on data from smart homes, we show how our statistical evaluation method for label refinements can be used in practice. Our method was able to select two label refinements out of a set of candidate label refinements that both had a positive effect on model precision.