Analyzr allows you to build models that fall into three broad categories:
- Propensity models: also known as classifiers, propensity models are a type of supervised machine learning and usually predict a binary outcome such as 'Will this prospect buy my product?' or 'Will this customer cancel her service?' The algorithms available in Analyzr to build a propensity model include:
- Logistic regression
- Random forest
- Gradient boosting
- XGBoost
- Adaptive boosting
- Extra trees
- Clustering models: clustering models are a type of unsupervised machine learning and usually identify natural groupings, segments, or clusters in your data. Analysts use clustering to group prospects and customers into similar groups for the purpose of customer segmentation. It can also be applied to other use cases such as segmenting survey responses, operational events, etc. The algorithms available in Analyzr to build a clustering model include:
- K-Means
- PCA / K-Means
- BIRCH
- DBSCAN
- Gaussian mixture
- Hierarchical agglomerative
- Mean shift
- OPTICS
- Spectral clustering
- Regression models: regression models are a type of supervised machine learning and usually predict a numerical variable such as total sales using a broad variety of inputs. The algorithms available in Analyzr to build regression model include:
- Linear regression
- Random forest regression
- Gradient boosting regression
- XGBoost regression
- LASSO regression