Iteratively partitions records into k clusters where each observation belongs to the cluster with the nearest mean. ![]() Use when the dataset isn't large and the number of clusters isn't known beforehand. Use with a large number of attributes to avoid collinearity (where multiple attributes are perfectly correlated) and overfitting.īuilds a hierarchy of clustering using either bottom-up (each observation is its own cluster and then merged) or top down (all observations start as one cluster) and distance metrics. Penalties of Lasso and Ridge regression methods. ![]() Provides additional information (regularization), performs variable selection, and performs linear combinations. Uses decision trees to predict both discrete and continuous values.Īdvanced regression model. To determine the best model, users can apply the model and visualize results of the calculations to determine accuracy, or they can open and explore the related datasets that Oracle Analytics used the model to output.Ĭonsult this table to learn about the provided algorithms: The user can choose the best model by comparing and weighing models against their own criteria. With these algorithms, users can create more than one model, or use different fine-tuned parameters, or use different input training datasets and then choose the best model. Oracle Analytics contains multiple machine learning algorithms for each kind of prediction or classification. For example, sometimes users choose models that have better overall accuracy, sometimes users choose models that have the least type I (false positive) and type II (false negative) errors, and sometimes users choose models that return results faster and with an acceptable level of accuracy even if the results aren't ideal. Normally users want to create multiple prediction models, compare them, and choose the one that's most likely to give results that satisfy their criteria and requirements. See Train a Predictive Model Using AutoML in Autonomous Data Warehouse. Note:If you're using data sourced from Oracle Autonomous Data Warehouse, you can use the AutoML capability to quickly and easily train a predictive model for you, without requiring machine learning skills.
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