10 JUL 2026 - Back up to full speed! Let's be honest: for the last few months, TorrentFunk was painfully slow. Pages crawled, searches dragged, and just loading the site tested everyone's patience. We hunted the problem down to our network and rebuilt it from the ground up — smarter caching, a much bigger and faster connection, and a lot of fine-tuning under the hood. The difference is night and day: the site now loads in a fraction of a second. No more waiting around. Thanks for sticking with us through the slow spell. Now go discover your funk!
Statistics and Machine Learning Toolbox provides functions and apps to describe, analyze, and model data. You can use descriptive statistics, visualizations, and clustering for exploratory data analysis, fit probability distributions to data, generate random numbers for Monte Carlo simulations, and perform hypothesis tests. Regression and classification algorithms let you draw inferences from data and build predictive models either interactively, using the Classification and Regression Learner apps, or programmatically, using AutoML. For multidimensional data analysis and feature extraction, the toolbox provides principal component analysis (PCA), regularization, dimensionality reduction, and feature selection methods that let you identify variables with the best predictive power. The toolbox provides supervised, semi-supervised and unsupervised machine learning algorithms, including support vector machines (SVMs), boosted decision trees, k-means, and other clustering methods. You can apply interpretability techniques such as partial dependence plots and LIME, and automatically generate C/C++ code for embedded deployment. Many toolbox algorithms can be used on data sets that are too big to be stored in memory
