Statistics and Machine Learning with KNIME
Build and deliver analytical models of any complexity.
Leverage broad & deep analytical capabilities
KNIME’s open platform allows you to access the most popular and most cutting-edge techniques on the market. Its intuitive, visual programming environment also allows you to perform every step of the data science lifecycle within one intuitive platform–from data ingestion and preprocessing to model development, validation, deployment, and monitoring.
Perform descriptive & inferential statistics
Identify trends, relationships, and outliers, enhance predictability and pattern analysis, and interpret your data with a wide range of statistical capabilities, including probability, hypothesis testing with t-tests and ANOVA, correlation and regression analysis, and forecasting. If needed, choose to integrate with R for scripting to extend these capabilities within the KNIME workflow environment.
Choose from a wide range of machine learning techniques
Access the most popular AI/ML techniques & libraries. Select and train many popular supervised learning algorithms for classification and regression such as decision trees, random forests, support vector machines, and unsupervised learning algorithms for clustering and association such as k-means clustering, DBSCAN, and principal component analysis. Extend capabilities with Python scripting within the KNIME workflow environment.
Implement feature engineering and selection
Use KNIME to improve the performance and interpretability of your ML model by creating new or transforming existing features. Create domain-specific and data-driven features. Preprocess data, handle missing values, normalize, scale, encode, and transform your features for a suitable representation of your ML model. Apply feature selection methods to identify the most relevant predictors and improve model efficiency.
Evaluate and interpret model performance
Evaluate and fine-tune model performance with a comprehensive set of performance metrics and visualization tools. Analyze classification metrics like confusion matrices, F1 scores, ROC curves, precision-recall plots, regression metrics like the mean score error, R2 score, and other evaluation metrics. Use Lime, SHAP, surrogate models, and other XAI techniques to explain model behavior.
Deploy and monitor machine learning models
Deploy machine learning models, together with their preprocessing steps, easily into production environments with KNIME’s integrated deployment capabilities. Monitor model performance, retrain models as needed, and ensure they continue to deliver accurate predictions with the KNIME CDDS extension.
Recommended resources
Machine Learning Cheat Sheet
A handy overview of the most popular nodes for training & deploying ML models.
Take a self-paced course on Machine Learning
Explore our learning paths and deepen your analytics skills.