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## Machine learning operators

Status: Delivered
by on ‎08-21-2017 05:14 AM - last edited on ‎08-24-2017 07:41 AM by

Implement Classification algorithms based on machine learning as Operators in Spatial Modeler. They can be used to perform multi-class prediction.

Options might include

 Classifier Algorithm When to use CART Decision Tree Decision Trees use a chain of simple decisions based on the results of sequential tests for class label assignment. The branches of the DT are composed of sets of decision sequences where tests are applied at the nodes of the tree and the leaves represent the class labels. ·        Simple and easy to understand ·        less influenced by outliers so good for classifying noisy data. K Nearest Neighbors Find the K nearest neighbors of each point, and assign the most occurring class to the point ·        Good for uniformly sampled data. Logistic regression ·        This is the go-to method for binary classification problems (Yes/No) ·        It is affected by noise. So a clean training data is needed.
 Naive Bayes A classification technique based on Bayes’ Theorem with an assumption of independence among predictors. Naive Bayes classifier assumes that the presence of a particular feature in a class is unrelated to the presence of any other feature. ·        When the assumption of independence holds, a Naive Bayes classifier performs better compared to other models and requires less training data. Radius Neighbor Find the neighbors within a fixed radius each point, and assign the most occurring class to the point ·        Better choice than K Nearest Neighbors when data is not uniformly sampled Random Forest In Random Forest, we grow multiple trees as opposed to a single tree in CART model.  Each tree gives a classification and we say the tree “votes” for that class. The forest chooses the classification having the most votes ·        Multiple trees as opposed to a single tree in CART model ·        considered to be a panacea of all data science problems. Generally start with this classifier and evaluate if the results are appropriate. Support vector Machine (SVM) Support Vector Machine (SVM) performs classification by constructing hyperplanes in a multidimensional space that separates different class, ·        Works extremmely well with clear margins of separation. Don't use if the target classes are overlapping ·        Does not perform well on highly skewed/imbalanced training data sets.
Status: Delivered

Delivered as part of ERDAS IMAGINE 2018, released March 1st, 2018

by
on ‎03-01-2018 10:52 AM
Status changed to: Delivered

Delivered as part of ERDAS IMAGINE 2018, released March 1st, 2018