Learn KNIME with Real Code Examples

// In KNIME Analytics Platform:
// 1. Add 'File Reader' node to load dataset
// 2. Use 'Partitioning' node to split train/test
// 3. Add 'Decision Tree Learner' or 'Random Forest Learner'
// 4. Connect to 'Predictor' node
// 5. Evaluate using 'Scorer' node
// Workflow executed visually with nodes connected via drag-and-drop.

An example illustrating a simple KNIME workflow for classification or regression tasks using the visual interface.

// Workflow steps:
// 1. File Reader -> load dataset
// 2. Partitioning -> split into train/test
// 3. Linear Regression Learner -> train model
// 4. Linear Regression Predictor -> predict on test
// 5. Numeric Scorer -> evaluate performance

Perform regression using KNIME visual nodes.

// Workflow steps:
// 1. File Reader -> load dataset
// 2. Cross Validation Loop Start
// 3. Decision Tree Learner -> train model inside loop
// 4. Predictor -> predict inside loop
// 5. Scorer -> evaluate inside loop
// 6. Cross Validation Loop End -> collect results

Using cross-validation to evaluate classifier performance in KNIME.

// Workflow steps:
// 1. File Reader -> load dataset
// 2. Normalizer -> normalize attributes
// 3. K-Means node -> set number of clusters
// 4. Hierarchical Clustering node -> optional
// 5. Cluster Assigner -> assign cluster labels
// 6. Data Views -> inspect clusters

Perform clustering using K-Means or Hierarchical nodes in KNIME.

// Workflow steps:
// 1. File Reader -> load dataset
// 2. Missing Value -> replace missing data
// 3. Column Filter -> select relevant features
// 4. String to Number -> convert categorical data
// 5. Normalizer -> normalize numeric attributes
// 6. Output -> preprocessed dataset

Cleaning and transforming data using KNIME preprocessing nodes.

// Workflow steps:
// 1. File Reader -> load dataset
// 2. Partitioning -> split data
// 3. Feature Selection Loop Start -> select attributes
// 4. Learner inside loop -> train model
// 5. Predictor -> predict
// 6. Feature Selection Loop End -> collect selected features

Selecting important features using KNIME feature selection nodes.

// Workflow steps:
// 1. File Reader -> load text data
// 2. Strings to Document -> convert text to documents
// 3. Preprocessing -> tokenize, remove stopwords, stem
// 4. Bag of Words -> create term matrix
// 5. Learner -> train classifier
// 6. Predictor -> predict
// 7. Scorer -> evaluate

Processing text data using KNIME Text Processing nodes.

// Workflow steps:
// 1. File Reader -> load dataset
// 2. Partitioning -> split data
// 3. Random Forest Learner -> train model
// 4. Predictor -> predict on test
// 5. Scorer -> evaluate performance
// 6. Optionally, combine multiple learners using Ensemble nodes

Using ensemble methods like Random Forest or Gradient Boosting in KNIME.

// Workflow steps:
// 1. Load trained model using Model Reader node
// 2. File Reader -> load new data
// 3. Predictor -> apply model to new data
// 4. Table Writer -> save predictions
// 5. Optionally, schedule workflow execution for automated scoring

Deploying a trained model in KNIME for batch prediction or scoring.

// Workflow steps:
// 1. File Reader -> load time series data
// 2. Lag Column -> create lagged features
// 3. Partitioning -> split train/test
// 4. ARIMA Learner or Exponential Smoothing Learner -> train model
// 5. Predictor -> forecast future values
// 6. Numeric Scorer -> evaluate forecast accuracy

Creating a time series forecasting workflow using KNIME nodes.