Spectrum Machine Learning
Spectrum Technology Platform Machine Learning provides the ability to group (bin) numeric data and fit supervised and unsupervised machine learning model data in those models.
Binning
Binning divides records into groups (bins) for a continuous variable without taking into account objective information. You can perform unsupervised binning in one of two ways: using equal-width bins or equal-frequency bins.
K-Means Clustering
K-Means Clustering creates models based on analytical clustering, which segments a set of records into clusters of similar records based on data values.
Linear Regression
Linear Regression performs machine learning by creating models from datasets that use continuous objectives with input variables.
Logistic Regression
Logistic Regression creates models from datasets that use binary objectives with input variables.
Principal Component Analysis
Principal Component Analysis is a statistical process that converts a set of observations of possibly correlated variables into a set of values of linearly uncorrelated variables known as principal components.
Random Forest Classification
Random Forest Classification performs machine learning by creating models from datasets that use continuous objectives with input variables.
Random Forest Regression
Random Forest Regression performs machine learning by creating models from datasets that use binary objectives with input variables.
Machine Learning Model Management
Machine Learning Model Management includes Model Assessment, which enables you to manage all machine learning models on your Spectrum Technology Platform server, and Binning Management, which enables you to manage all binning on your Spectrum Technology Platform server.