Build a Random Forest Classifier using CellProfiler image features (1)

0 ×

Extracted features from training images are read into the workflow. This data is annotated with a class variable and partitioned between training and validation. Hyperparameter optimization is carried out and parameters which perform the best are selected and used to train a deployment model. Features extracted from the deployment images are read into the workflow and classified. Predictions are saved to an Excel file.

This workflow contains the following components:
(1) Training data annotation
(2) Training and validation
(3) Hyperparameter optimization
(4) Deployment

URL: Decision Tree Node: Algorithm Settings https://youtu.be/CSwM92yTrJw

Nodes

• Column Rename4 ×
• Column Filter3 ×
• Random Forest Learner3 ×
• Random Forest Predictor3 ×
• DB Query Reader (Labs)2 ×
• Show all 16 nodes

Extensions

• FeatureKNIME Base nodes
• FeatureKNIME Database
• FeatureKNIME Ensemble Learning Wrappers
• FeatureKNIME Excel Support
• FeatureKNIME Javasnippet
• Show all 6 modules

Links

• No links available