Classifier Studio interface - Narrative I/O Knowledge Base

This reference documents the UI elements, configuration options, and actions available in the Classifier Studio interface.

Overview

Classifier Studio enables training classification models that categorize and label data within Narrative’s platform. It integrates data selection, label configuration, and compute resource allocation into a streamlined workflow. Path: My Models → Classifier Studio

Entry points

You can open Classifier Studio two ways:

Empty builder — Navigate to My Models → Classifier Studio and configure every step from scratch.
Pre-seeded from Label Studio — Clicking Train Model in a Label Studio project workspace opens Classifier Studio with the Dataset, Label column, and a single Feature already filled in: the project’s label dataset, label as the target, and input_string as a text feature. The seed reuses Label Studio’s existing source NQL expression, so no source-column materialization is needed before training. The handoff is gated on the project having at least 10 labeled rows across at least 2 distinct classes; below those thresholds Label Studio’s Train Model action stays disabled.

After a pre-seeded handoff you still pick the algorithm, hyperparameters, test/train split, and finalize the model name and tags — only the first three steps come pre-filled.

Classifier Studio and Label Studio are only available on Snowflake data planes — their seed, write, and training workflows rely on Snowflake-only DML — and the side navigation hides both entries on non-Snowflake planes (for example AWS).

Builder interactions

The builder is a step-by-step flow. Each configured step exposes inline actions so you can revise selections without navigating back through earlier steps:

Action	Description
Edit	Re-opens the step’s configuration view with your existing selections pre-filled
Remove	Clears the step’s configuration (and any dependent downstream steps)

When training is submitted, a compact toast notification confirms success and links directly to the Jobs page to track progress.

Dataset Selection module

The Dataset Selection module lets you choose the dataset that contains your labeled training data.

Element	Description
Select Dataset button	Opens the dataset selection view
Dataset name	Displays the currently selected training dataset

Dataset selection view

When you click Select Dataset, a full selection view appears with:

Element	Description
Dataset list	Searchable, virtualized list of available datasets in the current data plane
Next button	Proceeds to label column selection (enabled after selecting a dataset)
Cancel	Returns to the builder start view

Only datasets available in your currently selected data plane are shown.

Label Column module

The Label Column module lets you select the column that contains the classification target — the categories your model will learn to predict.

Element	Description
Select Label button	Opens the label column selection view (requires a dataset to be selected first)
Column name	Displays the currently selected label column

Label column selection view

Element	Description
Column dropdown	Filterable list of primitive-type columns from the selected dataset
Column type	Displays the data type next to each column name
Back button	Returns to dataset selection
Next button	Proceeds to feature configuration (enabled after selecting a column)

Supported column types

Only columns with primitive data types are available as label columns:

Type	Description
`string`	Text-based categories
`boolean`	Binary classification
`double`	Numeric labels
`long`	Integer labels
`timestamptz`	Timestamp-based labels

For best results, choose a column with a balanced distribution of category values in your training data.

Feature Configuration module

The Feature Configuration module lets you define which columns from your dataset serve as input features for the classifier and how they should be processed.

Element	Description
Configure button	Opens the feature configuration view
Feature list	Displays configured features and their types

Feature types

Type	Description
`text`	Free-form text processed via natural language techniques
`categorical`	Discrete categories encoded for model input
`numeric`	Continuous numeric values
`count_vectorizer`	Text converted to token frequency vectors
`embedding`	Pre-computed vector embeddings

Algorithm Configuration module

The Algorithm Configuration module lets you choose the classification algorithm for training.

Element	Description
Configure button	Opens the algorithm selection view
Algorithm name	Displays the selected classifier type

Available algorithms

Algorithm	Description
Logistic Regression	Linear model suited for binary and multi-class classification with interpretable results
Random Forest	Ensemble method that builds multiple decision trees for robust predictions

Hyperparameters

Each algorithm exposes its own set of tunable hyperparameters with sensible defaults. The configuration view surfaces the parameters relevant to your selected algorithm — for example, regularization strength for Logistic Regression, or tree count and maximum depth for Random Forest — so you can adjust only what matters for your use case.

Test/train split

Configure how the dataset is partitioned into training and evaluation sets:

Setting	Description
Test size	Fraction of rows reserved for evaluation
Random state	Integer seed that makes the split deterministic across retrains
Stratification	When enabled, preserves the label distribution in both the train and test splits — useful for imbalanced classes

Finalize module

The Finalize module is the last step before training. It lets you name and version the model, attach metadata, confirm the execution environment, and review everything you’ve configured in a single summary view.

Element	Description
Model name	Human-readable name for the trained classifier
Model version	Version identifier for this training run, enabling side-by-side comparison of retrains
Tags	Keywords for organizing and identifying trained classifiers
Data plane selector	Confirm the data plane where training executes
Configuration summary	Read-only review of your dataset, label column, features, algorithm, and split settings before submission

Classifier training runs on Snowflake’s built-in ML capabilities within your data plane. Data never leaves your infrastructure.

Actions reference

Configuration actions

Action	Location	Description	Result
Select Dataset	Dataset Selection module	Choose training dataset	Dataset selected, columns become available
Select Label	Label Column module	Choose target column	Classification target defined
Configure Features	Feature Configuration module	Define input features	Feature columns and types configured
Configure Algorithm	Algorithm Configuration module	Choose classifier type, hyperparameters, and split	Algorithm selected for training
Finalize	Finalize module	Name/version the model, add tags, confirm data plane, review summary	Training request fully configured

Training actions

Action	Location	Description	Result
Train Classifier	Page toolbar	Start training (enabled when all steps are configured)	Training job submitted and progress displayed

Training output

After you click Train Classifier:

A training job is submitted to the selected data plane
A success confirmation appears when the job is accepted
Monitor training progress on the Jobs page or in the Classifiers tab under My Models (see below)
The trained classifier becomes available for use in your data workflows

My Models — Classifiers tab

The Classifiers tab on the My Models page (My Models → Classifiers) lists the trained classifiers in your currently selected data plane. It is populated from models_train_classifier jobs, so every classifier you have ever trained appears here as soon as its job is submitted. Repeated training runs for the same name and version are collapsed into a single representative row, with the most recent run driving the row’s status, metrics, and timestamps. Path: My Models → Classifiers

Columns

Column	Description
Name	Registered classifier name. For runs that have not yet succeeded, the requested name from the training config is shown as a fallback.
Version	Registered classifier version, with the same fallback behavior as Name.
Status	Succeeded, Failed, Running, Pending, Scheduled, Cancelled, or Completed. A completed job whose result reports a training error is surfaced as Failed.
Accuracy	Overall accuracy from the training job’s minimal metrics, formatted as a percentage.
F1 (macro)	Macro-averaged F1 score from the training job’s minimal metrics, formatted as a percentage.
Classes	Number of target classes. Hover to see the class labels.
Runs	Number of training runs for this name and version rolled up into the row.
Trained (Local time)	End time of the most recent run (falls back to creation time for runs that have not yet ended), rendered in your local timezone.
Job ID	Hidden by default; searchable so you can look up a specific training run.

Filters and actions

Element	Description
Status filter	Multi-select filter with Succeeded, In progress (pending, scheduled, or running), and Failed.
Search	Substring search across the Name and Job ID columns.
Sort	Click the Name, Status, Runs, or Trained column headers to sort.
Refresh	Re-fetches classifier jobs for the current data plane. Overlapping refreshes are guarded so a stale response cannot replace current-plane results.

The listing is scoped to the selected data plane. Switching data planes from the top-level selector automatically re-scopes the results — classifiers trained in another plane will not appear until you switch back to it.

Workflow summary

Select dataset → Choose the training dataset in the Dataset Selection module
Select label column → Pick the column containing classification labels
Configure features → Define input features and their processing types
Configure algorithm → Choose between logistic regression and random forest, tune hyperparameters, and set the test/train split
Finalize → Name and version the model, add tags, confirm the data plane, and review the configuration summary
Train classifier → Click Train Classifier and monitor progress from the Jobs page

LLM Studio

Train and fine-tune LLM models using prepared datasets

Label Studio

Build labeling projects whose Train Model action seeds Classifier Studio

AI enrichment with NQL

Use AI functions in NQL queries for data classification and enrichment

Model Inference

Run inference using AI models within your data plane

Datasets

Understanding datasets in Narrative

​Overview

​Entry points

​Builder interactions

​Dataset Selection module

​Dataset selection view

​Label Column module

​Label column selection view

​Supported column types

​Feature Configuration module

​Feature types

​Algorithm Configuration module

​Available algorithms

​Hyperparameters

​Test/train split

​Finalize module

​Actions reference

​Configuration actions

​Training actions

​Training output

​My Models — Classifiers tab

​Columns

​Filters and actions

​Workflow summary

​Related content

LLM Studio

Label Studio

AI enrichment with NQL

Model Inference

Datasets

Overview

Entry points

Builder interactions

Dataset Selection module

Dataset selection view

Label Column module

Label column selection view

Supported column types

Feature Configuration module

Feature types

Algorithm Configuration module

Available algorithms

Hyperparameters

Test/train split

Finalize module

Actions reference

Configuration actions

Training actions

Training output

My Models — Classifiers tab

Columns

Filters and actions

Workflow summary

Related content