data preprocessing
Data Preprocessing for ML Models
This workflow demonstrates the following standard preprocessing steps before training a machine learning model:
- Partitioning
- Outlier detection
- Missing value handling
- Dimensionality reduction
- Conversion
- Feature selection
URL: House Prices Dataset https://www.kaggle.com/marcopale/housing
Nodes
- Reference Column Filter6 ×
- PCA Apply2 ×
- Partitioning2 ×
- Rule-based Row Filter2 ×
- Box Plot (JavaScript)1 ×
- CSV Reader1 ×
- Column Appender1 ×
- Column Filter1 ×
- Column Splitter1 ×
- Constant Value Column Filter1 ×
- Correlation Filter1 ×
- Feature Selection Filter1 ×
- Feature Selection Loop End1 ×
- Feature Selection Loop Start (1:1)1 ×
- GroupBy1 ×
- Linear Correlation1 ×
- Linear Regression Learner1 ×
- Math Formula1 ×
- Missing Value1 ×
- Missing Value (Apply)1 ×
- Missing Value Column Filter1 ×
- Normalizer1 ×
- Normalizer (Apply)1 ×
- Numeric Outliers1 ×
- Numeric Outliers (Apply)1 ×
- Numeric Scorer1 ×
- One to Many (PMML)1 ×
- PCA Compute1 ×
- PMML Transformation Apply1 ×
- Regression Predictor1 ×
- RowID1 ×
- Table Row to Variable1 ×
- Top k Row Filter1 ×
- Tree Ensemble Learner (Regression)1 ×
Extensions
- FeatureKNIME Base nodes
- FeatureKNIME Ensemble Learning Wrappers
- FeatureKNIME JavaScript Views
- FeatureKNIME Javasnippet
- FeatureKNIME Math Expression (JEP)
- Show all 7 modules
Links
- No links available