Use one of the most popular Machine Learning algorithms among data scientists - Random Forest - to understand which variables are more influential on the outcome of your interest and how. The outcome can be numeric, binary, or multiple categories (classes).
If you want to do Cohort Analysis right, use Survival Analysis, which employs a robust statistical algorithms 'Kaplan-Meier'. You can quickly find which cohorts have the problems in retaining customers, keeping employees, or keeping product qualities, for example, and understand the life time values of your customers.
Use Bayesian A/B Testing to evaluate and understand the result of your A/B Testing in a way you can explain to others with confidence.
Time Series Forecasting
With a cutting edge forecasting algorithm called Prophet built by a team at Facebook, you can quickly forecast the future for your time sensitive data even without a proper knowledge or training in time series forecasting.
Use Anomaly Detection to not only detect unusual activities like financial fraud, suspicious web site access, machine faults, etc., but also find what’s trending on your web sites, referrals, customer activities, etc.
Use Similarity Analysis to understand the similarities between categories and discover hidden characteristics of your customers, employees, products, etc.
Build, Predict, and Evaluate
Exploratory’s Machine Learning framework provides a simple and consistent experience for building, predicting, and evaluating the models from hundreds of the machine learning algorithms available in R.
Ensemble - Boosting / Bagging
Ensemble models such as Boosted Trees, Random Forest, etc. helps not only improve the prediction performance but also helps you find which attributes of the data have more impacts on the outcome you want to predict. You can quickly access to these state of art algorithms Data Scientists love without coding in Exploratory.
With Exploratory’s open machine learning framework you can bring your own favorite algorithms and run them natively, which means you can build, predict, and evaluate the models with point and clicks, just like any other out-of-the-box machine learning models.
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