What This Node Does
The Build ML Model node trains machine learning models on your data. Choose from classification algorithms (predict categories) or regression algorithms (predict numbers), configure model parameters, and train models ready for evaluation and deployment. [SCREENSHOT: Build ML Model node showing model training process]When to Use This Node
Use the Build ML Model node when you need to:- Train classification models - Predict categories (churn yes/no, product type, risk level)
- Train regression models - Predict numeric values (price, temperature, sales quantity)
- Try multiple algorithms - Compare Logistic Regression vs Random Forest vs XGBoost
- Create production models - Generate deployable model artifacts
Step-by-Step Usage Guide
1
Add Build ML Model node
2
Connect input data
Connect your prepared dataset to Build ML Model input[SCREENSHOT: Input data connected to Build ML Model]
3
Select model type and algorithm
Classification: Logistic Regression, Random Forest, XGBoost, Neural Network
Regression: Linear Regression, Decision Tree, Random Forest, XGBoost[SCREENSHOT: Algorithm selection]
4
Configure model parameters
Random Forest: n_estimators, max_depth, min_samples_split
XGBoost: learning_rate, max_depth, n_estimators
Neural Network: hidden_layers, learning_rate, epochs[SCREENSHOT: configuration]
5
Run and review
Tips and Best Practices
Start Simple: Begin with Logistic/Linear Regression as baseline, then try ensemble methods for improvement.
Feature Importance: Always generate feature importance to understand model decisions and validate feature engineering.
Compare Algorithms: Train 2-3 different algorithms, compare in Eval node, select best for production.