edarf: Exploratory Data Analysis using Random Forests

Exploratory Data Analysis using Random Forests

Although the rise of "big data" has made machine learning algorithms more visible and relevant for social scientists, they are still widely considered to be "black box" models that are not well suited for substantive research: only prediction. We argue that this need not be the case, and present one method, Random Forests, with an emphasis on its practical application for exploratory analysis a...

Analysis of missing data with random forests

Random Forests for Big Data

Big Data is one of the major challenges of statistical science and has numerous consequences from algorithmic and theoretical viewpoints. Big Data always involve massive data but they also often include data streams and data heterogeneity. Recently some statistical methods have been adapted to process Big Data, like linear regression models, clustering methods and bootstrapping schemes. Based o...

Loan Default Prediction on Large Imbalanced Data Using Random Forests

In this paper, we propose an improved random forest algorithm which allocates weights to decision trees in the forest during tree aggregation for prediction and their weights are easily calculated based on out-of-bag errors in training. Experiments results show that our proposed algorithm beats the original random forest and other popular classification algorithms such as SVM, KNN and C4.5 in t...

Classifying Evolving Data Streams Using Dynamic Streaming Random Forests

We consider the problem of data-stream classification, introducing a stream-classification algorithm, Dynamic Streaming Random Forests, that is able to handle evolving data streams using an entropy-based drift-detection technique. The algorithm automatically adjusts its parameters based on the data seen so far. Experimental results show that the algorithm handles multi-class problems for which ...