Preprocessing Node Catalog
Preprocessing category
Generated from 3 catalog nodes in AI/ML/Preprocessing.
Nodes in this category
Showing 3 of 3 matching nodes.
Apply Transform
AI/ML/PreprocessingApply a fitted transformer (Feature Scaler, TF-IDF) to a table, writing one vector per row. A Feature Scaler replays the exact offsets and scales learned at fit time, so applying it to train and test gives both the same statistics. TF-IDF is different: linfa recomputes the inverse document frequencies from the table being transformed, so vectors are only comparable within a single Apply Transform run.
Fit Feature Scaler
AI/ML/PreprocessingLearn per-feature offsets and scales from a training table. Distance- and gradient-based models (Logistic Regression, Elastic Net, SVM, KNN, Gaussian Mixture) only behave when their features share a scale.
Fit TF-IDF Vectorizer
AI/ML/PreprocessingLearn a vocabulary from a text column and turn documents into numeric vectors weighted by term frequency times inverse document frequency. Feed the fitted vectorizer to Apply Transform to vectorize a column, then train a classifier such as Multinomial Naive Bayes on the result. Tokenization always uses the built-in regex tokenizer, because a custom tokenizer function cannot be persisted and would make the saved model unloadable.