> ## Documentation Index
> Fetch the complete documentation index at: https://docs.kaireonai.com/llms.txt
> Use this file to discover all available pages before exploring further.
# Algorithm Models
> Manage scoring models (scorecard, bayesian, logistic_regression, gradient_boosted, thompson_bandit, epsilon_greedy, neural_cf, online_learner, external_endpoint). Train from real outcomes, score customers, score offer sets, upgrade model tiers, reset learning, and view evolution history.
## GET /api/v1/algorithm-models
List all algorithm models. Supports cursor-based pagination.
### Response
```json theme={null}
{
"data": [
{
"id": "model_001",
"key": "propensity-credit-card",
"name": "Credit Card Propensity",
"modelType": "bayesian",
"status": "active",
"version": 3,
"trainingSamples": 12500,
"metrics": { "auc": 0.78, "accuracy": 0.82 },
"lastTrainedAt": "2026-03-14T06:00:00.000Z",
"createdAt": "2026-01-20T10:00:00.000Z"
}
],
"pagination": {
"total": 5,
"hasMore": false,
"limit": 50,
"cursor": null
}
}
```
***
## POST /api/v1/algorithm-models
Create a new algorithm model.
### Request Body
| Field | Type | Required | Description |
| ----------------- | -------------- | -------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `key` | string | Yes | Unique model key |
| `name` | string | Yes | Display name |
| `modelType` | string | Yes | One of: `scorecard`, `bayesian`, `logistic_regression`, `gradient_boosted`, `thompson_bandit`, `epsilon_greedy`, `neural_cf`, `online_learner`, `external_endpoint`. |
| `description` | string | No | Description |
| `status` | string | No | Default: `"draft"` |
| `config` | object | No | Model-specific configuration |
| `targetField` | string | No | Target variable field name |
| `targetSchemaKey` | string | No | Schema key containing the target |
| `predictors` | array | No | Predictor feature definitions |
| `metrics` | object | No | Current model metrics (e.g., `{ auc, accuracy }`) |
| `metricsHistory` | array | No | Historical metrics snapshots |
| `modelState` | object | No | Model state (weights, priors, embeddings, etc.) |
| `learningConfig` | object | No | **Reserved metadata — not read by any trainer.** Accepted and persisted as-is, but no scoring/training code path reads it. Hyperparameters that actually affect training or scoring (learning rate, epochs, `priorAlpha`, `epsilon`, etc.) are type-specific per `modelType` and live in `config` instead. |
| `autoLearn` | boolean | No | Master switch for scheduled retraining. Default: `false`. When `false`, the scheduled-retrains cron skips this model entirely (bandits and online-learners ignore this flag — they always learn on every respond). |
| `learnMode` | string | No | One of `"none"`, `"incremental"`, `"scheduled"`, `"both"`. Default: `"none"`. `"scheduled"` enables the cron-driven offline retrain path; `"incremental"` is the per-respond update path (only meaningful for bayesian — bandits/online-learners do this regardless). |
| `learnSchedule` | string \| null | No | Cron expression (`"0 3 * * *"`) or interval shorthand (`"15m"`, `"6h"`, `"24h"`, `"7d"`). Default: `null` (→ 24h when `learnMode` is `"scheduled"`). |
`outcomeWeights`, `interactionFeatures`, and `evolutionConfig` are **not** accepted on create — the create schema silently drops them. Set them via `PUT /api/v1/algorithm-models/{id}` after the model exists.
**Defaults are intentionally conservative.** A POST with no overrides creates a fully inert model: `status: "draft"`, `registryStatus: "draft"`, `autoLearn: false`, `learnMode: "none"`. It exists in the DB but doesn't score traffic, isn't a champion, and won't retrain. To make it actually do something see the explicit 4-step path in [Model lifecycle](/ai-ml/model-lifecycle).
### Validation
All fields are validated via Zod schemas:
* `key`: 1-255 characters, must be unique per tenant.
* `name`: 1-255 characters.
* `modelType`: Must be one of the nine enum values listed above.
* `status`: Default `draft`. One of: `draft`, `active`, `paused`, `archived`.
* `config`, `metrics`, `modelState`, `learningConfig`: JSON objects (max 100 keys each; keys named `__proto__`, `constructor`, or `prototype` are rejected). These limits apply to every JSON-object field across the API.
* `predictors`, `metricsHistory`: JSON arrays (max 500 items each).
### Example
```bash theme={null}
curl -X POST https://playground.kaireonai.com/api/v1/algorithm-models \
-H "Content-Type: application/json" \
-H "X-Tenant-Id: my-tenant" \
-d '{
"key": "propensity-credit-card",
"name": "Credit Card Propensity",
"modelType": "scorecard",
"targetField": "converted",
"targetSchemaKey": "customers",
"predictors": ["income", "credit_score", "tenure_months"]
}'
```
### Error codes
| Code | Reason |
| ----- | ---------------------------------------------------------------------------------------------------------------------------- |
| `400` | Validation error (missing key or name, invalid modelType), or duplicate key (a model with this key and type already exists). |
| `415` | `Content-Type` is not `application/json`. |
**Response:** `201 Created`
***
## GET /api/v1/algorithm-models/{id}
Get model details including version history.
**Response:** `200 OK` with the model object and `versions` array.
***
## PUT /api/v1/algorithm-models/{id}
Update a model's configuration, status, metrics, or learning config.
### Request Body
All fields are optional. Only provided fields are updated.
| Field | Type | Description |
| --------------------- | ------ | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
| `name` | string | Updated name |
| `description` | string | Updated description |
| `status` | string | Updated status |
| `config` | object | Updated model config |
| `targetField` | string | Updated target field |
| `predictors` | array | Updated predictor list |
| `metrics` | object | Updated metrics |
| `modelState` | object | Updated model state (weights, coefficients) |
| `learningConfig` | object | Updated — but reserved metadata, not read by any trainer; use `config` to actually affect training/scoring |
| `outcomeWeights` | object | Map from outcome-type key to signed numeric weight. `null` falls back to `+1` for any outcome classified `"positive"` and `−1` for `"negative"`. **Misconfiguration silently inverts learning** — see [Model lifecycle: Configure outcome weights](/ai-ml/model-lifecycle#step-4-configure-outcome-weights). |
| `interactionFeatures` | object | Interaction feature configuration |
| `evolutionConfig` | object | Auto-evolution tier thresholds |
**Response:** `200 OK`
***
## DELETE /api/v1/algorithm-models?id={modelId}
Hard-deletes a model permanently. Unlike decisioning gates and contact policies, algorithm models use **hard delete** (the record is physically removed from the database).
### Query parameters
| Parameter | Required | Type | Description |
| --------- | -------- | ------ | ------------------- |
| `id` | **Yes** | string | Model ID to delete. |
### Error codes
| Code | Reason |
| ----- | ----------------------------- |
| `400` | Missing `id` query parameter. |
| `404` | Algorithm model not found. |
**Response:** `204 No Content`
***
## POST /api/v1/algorithm-models/{id}/score
Score a single customer against the model.
### Request Body
| Field | Type | Required | Description |
| ------------ | ------ | -------- | ------------------------- |
| `attributes` | object | No | Customer attribute vector |
### Response
The response shape depends on model type. The common field is `score`:
```json theme={null}
{
"score": 0.73
}
```
Additional fields may be present depending on model type:
| Field | Type | Present for | Description |
| -------------- | ------ | ------------------------------------------------------------------------------------------------------- | ------------------------------------- |
| `score` | number | All types | The computed score (0-1) |
| `confidence` | number | `bayesian` | Confidence level |
| `explanations` | array | `scorecard`, `bayesian`, `logistic_regression`, `gradient_boosted`, `thompson_bandit`, `epsilon_greedy` | Per-predictor or per-offer breakdowns |
There is no `contributions` array in the response. Use `explanations` for per-predictor or per-offer breakdowns (available for scorecard, bayesian, logistic\_regression, gradient\_boosted, thompson\_bandit, and epsilon\_greedy model types).
***
## POST /api/v1/algorithm-models/{id}/score-offer-set
Score a set of offers for a customer. Returns per-offer propensity scores with optional interaction history features.
### Request Body
| Field | Type | Required | Description |
| -------------------- | ------ | -------- | ------------------------------------------------------- |
| `customerAttributes` | object | No | Customer feature vector |
| `offers` | array | Yes | Array of `{ id, name?, attributes }` |
| `customerId` | string | No | If provided, fetches real interaction summaries from DB |
| `context` | object | No | Real-time context features |
### Response
Scores are returned sorted by `score` descending — the array index itself is the rank (index 0 = highest score).
```json theme={null}
{
"modelId": "model_001",
"modelKey": "propensity-credit-card",
"modelType": "bayesian",
"customerId": "CUST001",
"count": 3,
"scores": [
{ "offerId": "offer_001", "offerName": "Credit Card Premium", "score": 0.87, "interactionFeatures": { "impressions": 5, "positiveRate": 0.6 } },
{ "offerId": "offer_002", "offerName": "Savings Account", "score": 0.65 },
{ "offerId": "offer_003", "offerName": "Home Loan", "score": 0.42 }
]
}
```
There is no `rank` field in the response. The array order IS the ranking — index 0 is the highest-scored offer. If `customerId` is provided, `interactionFeatures` will be populated from the customer's recorded interaction history (impressions, click rate, conversion rate per offer).
***
## POST /api/v1/algorithm-models/{id}/train
Train (or retrain) a model using the per-model training route. Creates a new version snapshot and updates metrics history. Requires at least 50 interaction records in the tenant.
### Response
**200 OK** — Returns the updated model with incremented `version`, refreshed `metrics`, and `status` set to `"active"`.
**422 Unprocessable Entity** — Returned when the training request is well-formed but can't proceed:
| `error.code` | Cause |
| ------------------------ | --------------------------------------------------------------------------------------------------------------------- |
| `INSUFFICIENT_DATA` | Fewer than 50 interaction records to train from |
| `NO_PREDICTORS_SELECTED` | A `gradient_boosted` model has no selected predictors — add at least one before training |
| `MODEL_NOT_TRAINABLE` | The model type is scored via an external source and is not trained in-platform (`external_endpoint`, `onnx_imported`) |
**503 Service Unavailable** — `error.code: "ML_WORKER_UNAVAILABLE"`, `retryable: true`. Returned when a `gradient_boosted` model is trained but the Python [ML Worker](/self-host/deploy/ml-worker) (`ML_WORKER_URL`) is unreachable. The error message is surfaced verbatim ("Training requires the Python ml-worker service. Scoring remains available on the last trained model."), so the model keeps scoring on its previously-trained state. All in-process model types (`bayesian`, `scorecard`, `thompson_bandit`, `neural_cf`, `online_learner`) train without the worker and are unaffected.
Training requires interaction data recorded via the [Respond API](/api-reference/respond). New tenants with no interaction history will receive a 422 until enough outcomes are recorded. Models can still be used for scoring without training — scorecard models use bin-based rules, and other model types use default priors.
***
## POST /api/v1/algorithm-models/train
Bulk-train a model from real interaction outcomes. This is the recommended training endpoint — it replays the tenant's recorded outcomes through the model's training routine, updates `lastTrainedAt`, increments `version`, and creates a model version snapshot for rollback.
### Supported model types
All built-in model types are supported:
| Type | Training method |
| --------------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `scorecard` | Evaluates rule config against outcome data, computes confusion matrix metrics |
| `bayesian` | Replays outcomes through Bayesian updater (Naive Bayes with Laplace smoothing) |
| `logistic_regression` | Linear classifier trained in-process with SGD + L1/L2 regularization |
| `gradient_boosted` | LightGBM tree ensemble trained in the Python [ML Worker](/self-host/deploy/ml-worker); scored in-process in Node. Requires `ML_WORKER_URL` to be configured |
| `thompson_bandit` | Computes per-offer conversion rates as evaluation metrics |
| `epsilon_greedy` | Computes per-offer conversion rates as evaluation metrics |
| `neural_cf` | Trains two-tower embedding model with mini-batch SGD (binary cross-entropy) |
| `online_learner` | Streams through interactions one-by-one with SGD logistic regression |
### Request Body
| Field | Type | Required | Description |
| --------- | ------ | -------- | --------------------------- |
| `modelId` | string | Yes | Algorithm model ID to train |
### Response
```json theme={null}
{
"modelId": "model_001",
"modelType": "bayesian",
"sampleCount": 1250,
"metrics": {
"accuracy": 0.74,
"precision": 0.68,
"recall": 0.71,
"f1": 0.69,
"auc": 0.76
},
"status": "success"
}
```
### Error responses
Failures are classified rather than collapsed into a single status. The body carries `error.code` and (for transient conditions) `error.retryable`.
| Status | `error.code` | Condition |
| ------ | ------------------------ | ----------------------------------------------------------------------------------------------------------------------------------------------------- |
| `400` | — | Missing `modelId` |
| `422` | `INSUFFICIENT_DATA` | Fewer than 50 interactions in tenant |
| `422` | `NO_PREDICTORS_SELECTED` | `gradient_boosted` model with no selected predictors |
| `422` | `MODEL_NOT_TRAINABLE` | Model type is scored externally, not trained in-platform (`external_endpoint`, `onnx_imported`) |
| `503` | `ML_WORKER_UNAVAILABLE` | `gradient_boosted` training but the ML Worker (`ML_WORKER_URL`) is unreachable — `retryable: true`; the model keeps scoring on its last trained state |
| `500` | `INTERNAL_ERROR` | Genuine unexpected fault |
***
## POST /api/v1/algorithm-models/{id}/upgrade
Upgrade a model to the next tier along the progression `scorecard → bayesian → logistic_regression → gradient_boosted`. Optionally creates a champion/challenger experiment. Upgrading to `gradient_boosted` requires the [ML Worker](/self-host/deploy/ml-worker) to be reachable.
### Request Body
| Field | Type | Required | Description |
| ------------------ | ------- | -------- | ------------------------------------------------------------ |
| `createExperiment` | boolean | No | If `true`, auto-creates a 50/50 experiment. Default: `false` |
### Response
```json theme={null}
{
"upgraded": { "id": "model_002", "modelType": "bayesian", "status": "draft" },
"experiment": { "id": "exp_001", "name": "Credit Card Propensity: Champion vs Bayesian Upgrade" }
}
```
**Response:** `201 Created`
***
## GET /api/v1/algorithm-models/{id}/evolution-history
View model evolution config, progress toward the next tier, and a timeline of version transitions.
### Response
```json theme={null}
{
"modelId": "model_001",
"modelKey": "propensity-credit-card",
"currentModelType": "bayesian",
"evolutionConfig": {
"autoEvolve": true,
"currentTier": "bayesian",
"targetTier": "gradient_boosted",
"thresholds": {
"gradientBoostedSamples": 20000,
"gradientBoostedAuc": 0.80
}
},
"progress": {
"nextTier": "gradient_boosted",
"progressPct": 83,
"readyToEvolve": false,
"details": {
"samplesRequired": 20000,
"samplesActual": 13600,
"aucRequired": 0.80,
"aucActual": 0.78
}
},
"timeline": [
{ "version": 3, "modelType": "bayesian", "metrics": { "auc": 0.78 }, "createdAt": "2026-03-14T06:00:00.000Z" }
]
}
```
***
## POST /api/v1/algorithm-models/{id}/reset-offer
Reset or pause adaptive learning for a specific offer, category, channel, or globally.
### Request Body
| Field | Type | Required | Description |
| ------------ | ------ | -------- | ------------------------------------------------------------- |
| `action` | string | No | `"reset"` (default), `"pause"`, or `"resume"` |
| `scope` | string | No | `"offer"` (default), `"category"`, `"channel"`, or `"global"` |
| `offerId` | string | No | Offer to reset/pause (convenience alias for scopeId) |
| `categoryId` | string | No | Category to reset (resets all offers in category) |
| `channelId` | string | No | Channel to reset |
| `resetTo` | string | No | `"category_prior"` (default), `"global_prior"`, or `"zero"` |
| `reason` | string | No | Audit trail reason |
### Response
```json theme={null}
{
"modelId": "model_001",
"scope": "offer",
"scopeId": "offer_auto_renewal",
"action": "reset",
"resetTo": "category_prior",
"previousEvidence": 3241,
"newPrior": 0.281,
"reason": "Fixed qualification rules"
}
```
See [Adaptive Learning](/ai-ml/adaptive-learning) for full details on hierarchical learning, evidence decay, and PRIE scoring.
***
## POST /api/v1/algorithm-models/{id}/reset-learning
Reset a model's learned state back to fresh defaults. Creates a pre-reset version snapshot, clears metrics/metricsHistory, resets trainingSamples to 0, and sets status to `"draft"`.
Scorecard models have no learned state — calling reset-learning on a scorecard returns `400 Bad Request`.
### Response
**200 OK** — Returns the model with cleared state:
```json theme={null}
{
"id": "model_001",
"status": "draft",
"version": 4,
"metrics": {},
"metricsHistory": [],
"trainingSamples": 0,
"lastTrainedAt": null
}
```
***
## GET /api/v1/algorithm-models/{id}/adaptations
List the model's per-scope `ModelAdaptation` rows (the hierarchical offer → category → channel → global posteriors that drive adaptive learning). Useful for ops debugging and as the data source for the Model Health "Adaptations" panel.
### Query parameters
| Parameter | Required | Type | Description |
| --------- | -------- | ------ | ------------------------------------------------------------------------------ |
| `scope` | No | string | Filter to one scope: `offer`, `category`, `channel`, `direction`, or `global`. |
| `scopeId` | No | string | Exact `scopeId` to look up. Only applied when `scope` is also set. |
### Response
Rows are grouped under `byScope`, keyed by scope name. Each row carries `scope`, `scopeId`, `positives`, `negatives`, `evidence`, `positiveRate`, `paused`, `decayedAt`, and `updatedAt`.
```json theme={null}
{
"modelId": "model_001",
"modelKey": "propensity-credit-card",
"totalRows": 12,
"scopes": ["category", "global", "offer"],
"byScope": {
"offer": [
{ "scope": "offer", "scopeId": "offer_001", "positives": 340, "negatives": 812, "evidence": 1152, "positiveRate": 0.295, "paused": false, "decayedAt": null, "updatedAt": "2026-03-14T06:00:00.000Z" }
],
"category": [
{ "scope": "category", "scopeId": "cat_cards", "positives": 1204, "negatives": 3380, "evidence": 4584, "positiveRate": 0.263, "paused": false, "decayedAt": null, "updatedAt": "2026-03-14T06:00:00.000Z" }
],
"global": [
{ "scope": "global", "scopeId": "", "positives": 4102, "negatives": 11890, "evidence": 15992, "positiveRate": 0.256, "paused": false, "decayedAt": null, "updatedAt": "2026-03-14T06:00:00.000Z" }
]
}
}
```
### Error codes
| Code | Reason |
| ----- | ------------------------------------ |
| `404` | Algorithm model not found in tenant. |
***
## GET /api/v1/algorithm-models/{id}/uplift
Per-offer Conditional Average Treatment Effect (CATE) estimates for one customer, using T-learner or X-learner metalearners over the model's stored adaptations (marginal mode) or per-row-fitted base learners (fitted mode).
### Query parameters
| Parameter | Required | Type | Description |
| ------------ | -------- | ------ | ------------------------------------------------------------------------------------------------------------------------------- |
| `customerId` | **Yes** | string | Customer to estimate uplift for. |
| `method` | No | string | `t_learner` (default) or `x_learner`. |
| `offerIds` | No | string | Comma-separated offer IDs. Defaults to all `active` offers in the tenant. |
| `mode` | No | string | `marginal` (default) uses offer-scope vs category-scope adaptations; `fitted` fits per-row T/X-learners on interaction history. |
| `channelId` | No | string | Score-time channel context (fitted mode). |
| `direction` | No | string | `inbound` (default) or `outbound`. |
### Response
```json theme={null}
{
"customerId": "CUST001",
"modelId": "model_001",
"modelKey": "propensity-credit-card",
"method": "t_learner",
"mode": "marginal",
"scoreContext": { "channelId": null, "direction": "inbound" },
"offers": [
{
"offerId": "offer_001",
"offerName": "Credit Card Premium",
"tau": 0.0421,
"muT": 0.2951,
"muC": 0.253,
"segment": "persuadable",
"confidence": 0.63,
"evidenceOffer": 1152,
"evidenceCategory": 4584,
"modeUsed": "marginal",
"trainedN": null,
"xLearner": null
}
],
"ate": { "ate": 0.0421, "n": 1, "sd": 0 },
"classify": { },
"trainingRowsLoaded": 0,
"cacheHit": false,
"note": "Marginal CATE from offer-scope vs category-scope ModelAdaptation. Add `?mode=fitted` for per-row T-learner / X-learner training."
}
```
`segment` is one of `persuadable`, `sure_thing`, `lost_cause`, `sleeping_dog`, or `uncertain`. When no offers are in scope, `offers` is empty and a `note` explains why.
### Error codes
| Code | Reason |
| ----- | ----------------------------------------------------------------------- |
| `400` | Missing `customerId`, or `method`/`mode` not one of the allowed values. |
| `404` | Algorithm model not found in tenant. |
***
## GET /api/v1/models/{id}/registry
Read the model's current registry metadata: status, family label, last promotion timestamp, and snapshot metrics. The lookup is filtered by tenant.
This endpoint lives at `/api/v1/models/[id]/registry` — note the `models` prefix (not `algorithm-models`). It is the model-registry promotion surface, separate from the model CRUD routes documented above.
### Path Parameters
| Parameter | Type | Description |
| --------- | ------ | ----------------------------------------------- |
| `id` | string | Algorithm model ID — must belong to the tenant. |
### Response
```json theme={null}
{
"modelId": "model_001",
"modelName": "Credit Card Propensity",
"modelType": "bayesian",
"registry": {
"status": "champion",
"family": "credit-card-propensity",
"promotedAt": "2026-04-22T10:00:00.000Z",
"promotedBy": "alice@example.com",
"previousStatus": "challenger",
"metricsAtPromotion": { "auc": 0.79, "accuracy": 0.83 }
}
}
```
One of `draft`, `challenger`, `champion`, `archived`.
Family label that groups models for the "one champion per family" invariant. Defaults to the model name when not explicitly set.
### Status codes
| Code | When |
| ---- | -------------------------------------------- |
| 200 | Returns metadata |
| 400 | Missing `id` path segment |
| 401 | Caller is not authenticated |
| 403 | Caller is not `viewer`, `editor`, or `admin` |
| 404 | Model not found for tenant |
***
## POST /api/v1/models/{id}/registry
Promote or demote a model. Enforces a legal-transition matrix and the "one champion per family" invariant — promoting a model to `champion` demotes any existing champion in the same family inside the same database transaction. Every status change writes one audit-log entry (best-effort, after the core transaction commits).
### Legal status transitions
| From | Allowed to |
| ------------ | ------------------------------- |
| `draft` | `challenger`, `archived` |
| `challenger` | `champion`, `archived`, `draft` |
| `champion` | `archived`, `challenger` |
| `archived` | `draft` |
Any other transition is rejected with `400 Bad Request` and an error code of `invalid_transition`.
### Request Body
Target status. One of `draft`, `challenger`, `champion`, `archived`.
Operator id or system label for the audit row. Defaults to `"system"` when omitted.
Map of `metric → number` recorded alongside the promotion (e.g., `{ auc: 0.79, accuracy: 0.83 }`). Used by the W6.2 auto-rollback guard to compare a candidate champion against the incumbent.
### Response
```json theme={null}
{
"modelId": "model_001",
"previousStatus": "challenger",
"newStatus": "champion",
"demotedChampionId": "model_old_001"
}
```
Present only when the promotion to `champion` displaced an incumbent in the same family. The incumbent's status is set to `challenger` in the same transaction.
Present when the W6.2 rollback guard reported breached thresholds but the promotion was still permitted (e.g., `bypassRollbackGuard: true`). Each entry: `{ signal, value, threshold }`.
### Status codes
| Code | When |
| ---- | ------------------------------------------------------------- |
| 200 | Promotion succeeded |
| 400 | Invalid body or illegal status transition |
| 401 | Caller is not authenticated |
| 403 | Caller is not `admin` |
| 404 | Model not found for tenant |
| 429 | Rate limit exceeded — 60 promotions per 60 seconds per tenant |
| 500 | Unexpected error |
### Roles
POST: admin only. GET: admin, editor, viewer.
***
## Roles
| Endpoint | Allowed Roles |
| ----------------------------------- | --------------------- |
| `GET /algorithm-models` | admin, editor, viewer |
| `POST /algorithm-models` | admin, editor |
| `PUT /algorithm-models/{id}` | admin, editor |
| `DELETE /algorithm-models/{id}` | admin, editor |
| `POST /{id}/score` | any authenticated |
| `POST /{id}/score-offer-set` | any authenticated |
| `POST /{id}/train` | admin, editor |
| `POST /algorithm-models/train` | admin, editor |
| `POST /{id}/upgrade` | admin, editor |
| `POST /{id}/reset-offer` | admin, editor |
| `POST /{id}/reset-learning` | admin, editor |
| `GET /{id}/adaptations` | admin, editor, viewer |
| `GET /{id}/uplift` | admin, editor, viewer |
| `GET /{id}/evolution-history` | any authenticated |
| `GET /api/v1/models/{id}/registry` | admin, editor, viewer |
| `POST /api/v1/models/{id}/registry` | admin |
See also: [Algorithms & Models](/ai-ml/algorithms)