---
title: "SegmentML Model Fetch"
method: GET
path: "/api/segmentml/{id}"
tags: ["SegmentML"]
---

# SegmentML Model Fetch

`GET /api/segmentml/{id}`

## Request

- Base URL: `https://api.lytics.io`
- URL: `https://api.lytics.io/api/segmentml/{id}`
- Auth: API key in header `Authorization`

Get a SegmentML model.

Additional atttributes from a completed SegmentML model GET response:

| field                 | DataType         | Description   |
|-----------------------|------------------|---------------|
| features: kind        | string           | The field is either a Lytics Segment feature (*segment*), a lql/user-field feature (*lql*), a Lytics Behavioral Score feature (*score*), or a Lytics Content Affinity feature (*content*)
| features: fieldtype   | string           | Field type is either *numeric* or *categorical*
| features: name        | string           | The name of a field
| features: importance  | number           | The relative importance of a field in the model
| features: correlation | number           | Correlation between specific field and target
| features: impact      | object           | The impact object details the Lift and shows the marginal effect of a feature on the predicted outcome of the model
| mse                   | number           | Mean-squared error value
| rsq                   | number           | R-squared value or coefficient of determination
| false_negative        | number           | The number of users in the source segment who are predicted to be in the target segment.
| false_positive        | number           | The number of users in the target segment who are not predicted to be in the target segment.
| true_negative         | number           | The number of users in the source segment who are not predicted to be in the target segment.
| true_positive         | number           | The number of users in the target segment who are predicted to be in the target segment.
| success               | []number         | Number of successful predictions for a given prediction value
| failure               | []number         | Number of failed predictions for a given prediction value
| auc                   | number           | Area under the ROC curve
| threshold             | number           | Optimal decision threshold to minimize false-positives and false-negatives
| accuracy              | number           | A value that represents the accuracy of the model; scale ranges from 0 (least accurate) to 10 (most accurate).
| reach                 | number           | A value that represents the number of source users that look like target users; scale ranges from 0 (low reach) to 10 (high reach).
| model_health          | number           | The overall health of the model (i.e. "healthy", "unhealthy")
| msgs                  | number           | Messages for the user about the model with levels of severity (i.e. "debug", "info", "warn", "error")

To learn more about the metrics false negative, false positive etc., check out [binary classification](https://en.wikipedia.org/wiki/Binary_classification).

```
# Curl example of getting a SegmentML model
curl -s -J -XGET "https://api.lytics.io/api/segmentml/all::smt_power" -H "Authorization: $LIOKEY"
```

## Path parameters

- `id` string, required

## Query parameters

- `account_id` string

## Response `200`

OK

---

[API](https://skmtc.dev/lytics/apis/v1-lytics-api.md) - [All operations](https://skmtc.dev/lytics/apis/v1-lytics-api/llms.txt) - [OpenAPI document](https://skmtc.dev/lytics/apis/v1-lytics-api/revisions/339fd1d88486?raw)