Model Builder

Model Builder

AutoStat®’s engine room. Undertake a range of advanced analytics with your data, and apply machine learning, statistics, time-series and survival modelling without needing to code. Bayesian optimisation with AutoML for automated variable selection and parameter tuning to enhance model performance.

 AutoStat®animation

01 - Statistical Frameworks

Model a wide range of data using linear regression & mixed models, along with a range of generalised linear models, including Logistic, Probit, Poisson, Logit and Tobit, plus extensions for mixed effects models.

02 - Machine Learning and Clustering

Access a range of supervised and unsupervised machine learning algorithms for both regression and classification problems. Apply clustering with Gaussian mixture models and a number of ML clustering algorithms.

03 - Multivariate Modelling

Make prediction models across many combinations of variables. Including dynamic factor models, factor analysis, multiple regression and linear discriminant analysis.

04 - Time-Series Modelling

Extract forecasts and identify trends with some of the most advanced time-series forecasting models including ARIMA, S-ARIMA, and Bayesian Unobserved Component models.

05 - Survival Analysis

Analyse failure points and durations of variables using a range of parametric and non-parametric algorithms.

06 - Results Analytics

Analyse model performance; identify key variables and their relative importances; and extract predictions for real-world insights.

AutoStat® Features

DATA MANAGER
VISUALISATIONS
MODEL BUILDER
NATURE LANGUAGE PROCESSING
R INTEGRATION
DASHBOARDS

Experience AutoStat®

From data processing and visualisation, to machine learning and beyond, get up to speed with the most advanced data science techniques with real-world projects using AutoStat®.

Ensemble Machine Learning

Applying ML techniques to predict Heart Disease

Regression Models in Practice

A deep-dive into frequentist and Bayesian regression models

Mixed Effects Modelling

Modelling chick weights over time with fixed and random effects

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