Tuning Node Catalog
Tuning category
Generated from 4 catalog nodes in AI/ML/Tuning.
Nodes in this category
Showing 4 of 4 generated node docs.
Auto Classifier
AI/ML/TuningAutomatically finds the best classification model. Cross-validates Naive Bayes, Decision Tree, Logistic Regression, Random Forest and SVM, then retrains the winner on the full dataset. The reported Best Model Type can be fed straight into Grid Search to tune it further.
Auto Ordinal
AI/ML/TuningAutomatically finds the best model for a target whose levels are ORDERED (1 < 2 < ... < 5, or low < medium < high). Cross-validates the ordinal families - Proportional Odds and Ordered Probit, the all-threshold model and its support-vector form, Ordinal Ridge, Continuation Ratio and Adjacent Category, plus an optional rank-consistent neural family that is off by default because it costs far more than all the others combined - on identical folds, ranks them by an ordinal metric that knows how far a miss was, then retrains the winner on the full data. Use this rather than Auto Classifier, which resolves the target without its order and ranks by accuracy or macro-F1, scoring a five-level miss exactly like a one-level one. Every candidate here is a gradient or a least-squares fit on the raw columns, so scale your features with the Fit Feature Scaler node first: unscaled columns change which family wins, not just how fast it converges.
Grid Search
AI/ML/TuningExhaustive search over parameter combinations with cross-validation. Returns the best parameters found. Model Type accepts the same names the Auto Classifier reports as its best model, so the two nodes chain directly.
Ordinal Grid Search
AI/ML/TuningExhaustively searches the hyperparameters of ONE ordinal model family with cross-validation, for a target whose levels are ORDERED (1 < 2 < ... < 5, or low < medium < high). Every combination in the Parameter Grid is scored on the SAME folds and ranked by an ordinal metric that knows how far a miss was. Use this rather than Grid Search, which resolves the target without its order and tunes against accuracy, scoring a five-level miss exactly like a one-level one. Model Type accepts the names Auto Ordinal reports as its best model, so the usual chain is Auto Ordinal to pick the family, then this node to tune it. Every family here is a gradient or a least-squares fit on the raw columns, so scale your features with the Fit Feature Scaler node first: unscaled columns change which hyperparameters win, not just how fast they converge.