ML Node Catalog

Fit/Train a Decision Tree classifier. Native multi-class support with interpretable rules.

7 in2 outSecurity 6/10

Train Classifier (K-Nearest Neighbours)

Fit 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.

4 in2 outSecurity 3/10

Train Classifier (Logistic Regression)

Fit/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.

8 in2 outSecurity 6/10

Train Classifier (Multinomial Naive Bayes)

Fit/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.

3 in2 outSecurity 6/10

Train Classifier (Naive Bayes)

Fit/Train a Gaussian Naive Bayes classifier. Native multi-class support - no need for One-vs-All.

2 in2 outSecurity 6/10

Train Classifier (Random Forest)

Fit/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.

8 in2 outSecurity 6/10

Train Classifier (SVM)

Fit/Train Support Vector Machines (SVM) for Multi-Class Classification

5 in2 outSecurity 6/10

Fit Clustering (Gaussian Mixture)

AI/ML/Clustering

Fit/Train a Gaussian Mixture Model. Soft clustering with per-component covariances and mixture weights, fitted by Expectation-Maximization.

10 in3 outSecurity 6/10

Train Clustering (DBSCAN)

AI/ML/Clustering

Fit/Train DBSCAN Density-Based Clustering

4 in3 outSecurity 6/10

Train Clustering (KMeans)

AI/ML/Clustering

Fit/Train KMeans Clustering

3 in2 outSecurity 6/10

K-Fold Split

Generate K train/test splits for cross-validation. Each fold uses (K-1)/K data for training and 1/K for validation, and runs the connected fold branch once per fold.

6 in4 outSecurity 6/10

Sample Dataset

Random sample N records or a ratio from a dataset

5 in2 outSecurity 6/10

Shuffle Dataset

Shuffle dataset rows randomly

3 in1 outSecurity 6/10

Split Dataset

Split a dataset into training and testing subsets

5 in1 outSecurity 6/10

Stratified Split

Split a dataset into training and testing subsets, keeping every class at its original proportion in both subsets

7 in1 outSecurity 6/10

Accuracy

Calculate classification accuracy by comparing predictions to actual values

4 in2 outSecurity 8/10

Confusion Matrix

Build confusion matrix and calculate precision, recall, and F1 score

4 in2 outSecurity 8/10

ROC-AUC & Log Loss

Threshold-free evaluation of a binary classifier: area under the ROC curve, log loss and the curve points. This is the payoff for Logistic Regression producing calibrated probabilities instead of bare class labels.

5 in4 outSecurity 8/10

Regression Metrics

Calculate MSE, RMSE, MAE, and R² for regression predictions

4 in2 outSecurity 8/10

Silhouette Score

Evaluate clustering quality: how much closer each sample sits to its own cluster than to the nearest other one (-1 to +1)

5 in4 outSecurity 8/10

Feature Importance

Extract per-feature importance from a Decision Tree, Random Forest or AdaBoost model

3 in4 outSecurity 10/10

Get Centroids

Extract cluster centroids from a trained KMeans model

2 in2 outSecurity 10/10

Get Coefficients

Extract coefficients and intercept from a trained Linear Regression model

2 in2 outSecurity 10/10

Model Info

2 in3 outSecurity unrated

Get general information about any ML model

Extract Keypoint

2 in5 outSecurity unrated

Extract a specific keypoint from a pose by index or name

Feature Extraction

4 in3 outSecurity unrated

Extract feature vectors from images using ONNX models

Feature Similarity

2 in2 outSecurity unrated

Compare two feature vectors using cosine similarity or L2 distance

Image Classification

7 in2 outSecurity unrated

Image Classification with ONNX-Models. Download models from: MobileNetV2 (https://github.com/onnx/models/tree/main/validated/vision/classification/mobilenet), SqueezeNet (https://github.com/onnx/models/tree/main/validated/vision/classification/squeezenet), ResNet (https://github.com/onnx/models/tree/main/validated/vision/classification/resnet), EfficientNet (https://github.com/onnx/models/tree/main/validated/vision/classification/efficientnet-lite4)

Load ONNX

2 in4 outSecurity unrated

Load ONNX Model from Path

Model Info

2 in4 outSecurity unrated

Get ONNX model metadata (inputs, outputs, shapes)

Object Detection

6 in2 outSecurity unrated

Object Detection in Images with ONNX-Models. Download models from: TinyYOLOv2 (https://github.com/onnx/models/tree/main/validated/vision/object_detection_segmentation/tiny-yolov2), YOLO (https://github.com/onnx/models/tree/main/validated/vision/object_detection_segmentation), SSD-MobileNet (https://github.com/onnx/models/tree/main/validated/vision/object_detection_segmentation/ssd-mobilenetv1)

Pose Estimation

5 in2 outSecurity unrated

Detect human poses and keypoints using ONNX models. Download models from: YOLOv8-Pose (https://docs.ultralytics.com/models/yolov8/), MoveNet (https://tfhub.dev/google/movenet/), HRNet (https://github.com/OAID/TengineKit)

Semantic Segmentation