Classification Node Catalog
Classification category
Generated from 9 catalog nodes in AI/ML/Classification.
Nodes in this category
Showing 9 of 9 generated node docs.
Fit Novelty Detection (One-Class SVM)
AI/ML/ClassificationFit a One-Class SVM on normal observations only. Predictions flag whether a new row is an inlier (1) or an outlier (0).
Train Classifier (AdaBoost)
AI/ML/ClassificationFit/Train an AdaBoost classifier using multi-class SAMME boosting over shallow Decision Trees. Each learner focuses on the rows its predecessors got wrong, so boosting usually beats a single tree on weak signal, but it is far more sensitive to label noise and outliers than Random Forest. Estimators is a maximum, not a guarantee: boosting stops early once a learner is no better than random guessing.
Train Classifier (Decision Tree)
AI/ML/ClassificationFit/Train a Decision Tree classifier. Native multi-class support with interpretable rules.
Train Classifier (K-Nearest Neighbours)
AI/ML/ClassificationFit a K-Nearest-Neighbours classifier. Non-parametric and instance based: the fitted model embeds a verbatim copy of the whole training set instead of learned coefficients, so every training row (and any personal data in it) travels with the model, is written into every saved model file and can be reconstructed by anyone holding it. Treat the model with the same care as the source table.
Train Classifier (Logistic Regression)
AI/ML/ClassificationFit/Train a Logistic Regression classifier with L2 regularization. Handles binary and multi-class targets and yields interpretable coefficients plus calibrated probabilities. The solver expects features on a comparable scale - fit a Feature Scaler first if your columns have very different ranges.
Train Classifier (Multinomial Naive Bayes)
AI/ML/ClassificationFit/Train a Multinomial Naive Bayes classifier, the standard baseline for text and other count data. Features must be non-negative counts or TF-IDF weights, which is what the Fit TF-IDF Vectorizer node produces. Native multi-class support and a single pass over the data.
Train Classifier (Naive Bayes)
AI/ML/ClassificationFit/Train a Gaussian Naive Bayes classifier. Native multi-class support - no need for One-vs-All.
Train Classifier (Random Forest)
AI/ML/ClassificationFit/Train a Random Forest classifier: many Decision Trees, each grown on a bootstrapped sample of the rows and a random subset of the features, combined by majority vote. Far more robust to overfitting than a single tree, at the price of interpretability. Model size and fit time grow linearly with Ensemble Size, so a forest of 500 trees costs roughly 500x a single tree.
Train Classifier (SVM)
AI/ML/ClassificationFit/Train Support Vector Machines (SVM) for Multi-Class Classification