Machine learning is gaining popularity as an essential way of not only identifying patterns and relationships, but also predicting outcomes. This is creating a fundamental shift in the way businesses are operating—from being reactive to being proactive. Unfortunately, the growing velocity, volume, and variety of data has increased the complexity of building predictive models, since few tools are capable of processing these massive data sets at the speed of business. HPE Vertica’s in-database machine learning allows you to embrace the power of Big Data and accelerate business outcomes with no limits and no compromises.
The K-Means algorithm is a type of unsupervised learning algorithm, meaning the input data is unlabeled. The algorithm takes the unlabeled data and clusters the data points into different clusters based on similarities between the data points.
The Logistic Regression algorithm is used to classify data into groups based on the logical relationship between independent variables, or features, and some dependent variable, or outcome. The outcome of logistic regression is a binary value which represents an outcome such as true/false, pass/fail, yes/no, 1/0.
The Linear Regression algorithm is used to predict continuous numerical outcomes in linear relationships along a continuum. Using linear regression, you can model the linear relationship between independent variables, or features, and a dependent variable, or outcome.