Retrieve an outcome's analysis

gethttps://api.faraday.ai/v1/outcomes/{outcome_id}/analysis

Get details on a specific outcome's analysis report on model performance, bias, etc.

Authentication

Bearer Authentication

Provide your API key in the Authorization header. You can find your API key in the Settings page of the dashboard.

Authorization: Bearer YOUR_TOKEN

path parameters

outcome_idstring<uuid>required

The UUID of an outcome

Responses

200Information about the outcome analysis

biasobject

Information about the various types of bias for an outcome.
The measurements for each type of bias are taken with respect to
subpopulations of sensitive dimensions whose priority
can be increa...

Properties of object

dataobjectrequired

The underlying data used to build an outcome can introduce bias by unevenly representing subpopulations.
This bias is measured by comparing distributions of sensitive dimensions across labels.
Categor...

Properties of object

ageobjectrequired

The age distributions among positive examples, candidates, and negative examples
along with a level that describes the bias.

Properties of object

candidatesarray[object]required

The age distribution for a given label (positives, candidates, negatives).

Items of array[object]

xnumberrequired

Age in years.

ynumberrequired

The approximate density of individuals with a given age normalized to [0,1].

levelstringrequired

Let w(l) denote the Wasserstein distance between the age distribution of individuals with label l
and the age distribution of all individuals.
Specifically, the implement

Let w denote the maxim...

Allowed values: low_bias, moderate_bias, strong_bias

negativesarray[object]

The age distribution for a given label (positives, candidates, negatives).

Items of array[object]

xnumberrequired

Age in years.

ynumberrequired

The approximate density of individuals with a given age normalized to [0,1].

positivesarray[object]required

The age distribution for a given label (positives, candidates, negatives).

Items of array[object]

xnumberrequired

Age in years.

ynumberrequired

The approximate density of individuals with a given age normalized to [0,1].

genderobjectrequired

The proportions of gender values (female, male, unknown) among positive examples, candidates, and negative examples
along with a level that describes the bias.

Properties of object

candidatesarray[object]required

The proportions of gender values for a given label (positives, candidates, negatives).

Items of array[object]

xstringrequired

The gender value (female, male, unknown).

ynumberrequired

The proportion of individuals with the given gender value among the individuals with the given label.

levelstringrequired

Let d(g,l) denote the absolute difference between the two proportions:

the proportion of individuals with gender g among individuals with label l
the proportion of individuals with gender `g...

Allowed values: low_bias, moderate_bias, strong_bias

negativesarray[object]

The proportions of gender values for a given label (positives, candidates, negatives).

Items of array[object]

xstringrequired

The gender value (female, male, unknown).

ynumberrequired

The proportion of individuals with the given gender value among the individuals with the given label.

positivesarray[object]required

The proportions of gender values for a given label (positives, candidates, negatives).

Items of array[object]

xstringrequired

The gender value (female, male, unknown).

ynumberrequired

The proportion of individuals with the given gender value among the individuals with the given label.

fairnessarray[object]required

A subpopulation is a subset of the eligible population defined by a set of
sensitive dimensions (e.g. age and gender) and values (e.g. adult and female).

Each subpopulation has a set of metrics that ...

Items of array[object]

dimensionsarray[string]required

A nonempty ordered set of sensitive dimensions.

metricsarray[object]required

A metric that measures bias for a subpopulation.
There are 3 groups of metrics detailed here.
A decision threshold corresponding to a top 5% target population is chosen for all metrics.

The metrics m...

Items of array[object]

levelstringrequired

An interpretation of the metric value for the subpopulation.

namestringrequired

The name of the metric.

valuenumberrequired

The value of the metric.

valuesarray[string]required

A nonempty ordered set of values for each corresponding sensitive dimension.

powerarray[object]required

A subpopulation is a subset of the eligible population defined by a set of
sensitive dimensions (e.g. age and gender) and values (e.g. adult and female).

Each subpopulation has a set of metrics that ...

Items of array[object]

dimensionsarray[string]required

A nonempty ordered set of sensitive dimensions.

metricsarray[object]required

A metric that measures bias for a subpopulation.
There are 3 groups of metrics detailed here.
A decision threshold corresponding to a top 5% target population is chosen for all metrics.

The metrics m...

Items of array[object]

levelstringrequired

An interpretation of the metric value for the subpopulation.

namestringrequired

The name of the metric.

valuenumberrequired

The value of the metric.

valuesarray[string]required

A nonempty ordered set of values for each corresponding sensitive dimension.

predictionsarray[object]required

A subpopulation is a subset of the eligible population defined by a set of
sensitive dimensions (e.g. age and gender) and values (e.g. adult and female).

Each subpopulation has a set of metrics that ...

Items of array[object]

dimensionsarray[string]required

A nonempty ordered set of sensitive dimensions.

metricsarray[object]required

A metric that measures bias for a subpopulation.
There are 3 groups of metrics detailed here.
A decision threshold corresponding to a top 5% target population is chosen for all metrics.

The metrics m...

Items of array[object]

levelstringrequired

An interpretation of the metric value for the subpopulation.

namestringrequired

The name of the metric.

valuenumberrequired

The value of the metric.

valuesarray[string]required

A nonempty ordered set of values for each corresponding sensitive dimension.

summaryobjectrequired

A summary of each type of bias distilled down to a boolean value.

Properties of object

data_unbiasedbooleanrequired

This boolean value indicates whether the underlying data used to build an outcome is unbiased.

Each sensitive dimension has a similarity metric (taking values in [0,1])
to measure the discrepancy b...

fairness_fairbooleanrequired

This boolean value indicates that all subpopulations are neither privileged nor disadvantaged using a sum of 4 metrics defined in

Bellamy, Rachel KE, et al. "AI Fairness 360: An extensible toolkit f...

power_unbiasedbooleanrequired

This boolean value indicates whether the predictive performance of an outcome is unbiased across subpopulations.

To measure predictive performance, the F1 score, the harmonic mean of precision and re...

predictions_unbiasedbooleanrequired

This boolean value indicates that all subpopulations are equally represented among positive predictions.

To measure this, a decision threshold corresponding to a top 5% target population is chosen,
a...

overall_featuresobjectrequired

Properties of object

all_individualsarray[object]

Items of array[object]

directionalityarray[object]required

importancenumberrequired

The importance of the feature

literatestringrequired

The human-readable name of the feature

namestringrequired

The identifier for the feature

recognized_individualsarray[object]required

Items of array[object]

directionalityarray[object]required

importancenumberrequired

The importance of the feature

literatestringrequired

The human-readable name of the feature

namestringrequired

The identifier for the feature

unrecognized_individualsarray[object]

Items of array[object]

directionalityarray[object]required

importancenumberrequired

The importance of the feature

literatestringrequired

The human-readable name of the feature

namestringrequired

The identifier for the feature

overall_performanceobjectrequired

Properties of object

all_individualsobject

Performance metrics for a propensity model, optionally over a tenure range.

Properties of object

lift_curvearray[object]required

Items of array[object]

xnumberrequired

The quantile-based target percentile. For example, a value of .1 defines a target percentile specifying the top 10 percent of scores.

Example: 0.1

ynumberrequired

The estimated lift over random at the given target percentile.

Example: 0.75

y_maxnumberrequired

The upper error bound for y.

Example: 0.76

y_minnumberrequired

The lower error bound for y.

Example: 0.74

lift_tableobjectrequired

A table of arbitrary data. Purposefully untyped to allow for flexibility in the data.

Properties of object

headersarray[string]required

The headers of the table. Similar to a CSV header row.

rowsarray[array]required

The rows of the table. Similar to a CSV data row. Each row must have the same number of columns as the headers array. The order of the columns must match the order of the headers.

lift_valuenumber

Average of lift over random at average conversion rate (r), (0.75 * r), (0.5 * r), (0.25 * r).

Example: 1.425

metrics_tableobjectrequired

A table of arbitrary data. Purposefully untyped to allow for flexibility in the data.

Properties of object

headersarray[string]required

The headers of the table. Similar to a CSV header row.

rowsarray[array]required

The rows of the table. Similar to a CSV data row. Each row must have the same number of columns as the headers array. The order of the columns must match the order of the headers.

roc_aucnumberrequired

The area under the receiver operating characteristic curve.

Example: 0.75

roc_curvearray[object]required

Items of array[object]

xnumberrequired

A given false positive rate.

Example: 0.5

ynumberrequired

The estimated true positive rate corresponding to x.

Example: 0.75

strategy_idstring<uuid>

The ID of a strategy from the strategies array. If the group is overall, this value is null. To determine which strategies apply in such cases, refer to the strategy IDs defined at the tenure level.

Example: "b80217ae-7628-4c66-8a19-5bf452495c39"

tenure_endinteger

When the tenure range ends measured in days. The value can be null if no tenure range applies to this model. The value can also be null if the range is unbounded.

Example: 60

tenure_startinteger

When the tenure range starts measured in days. The value can be null if no tenure range applies to this model.

Example: 30

recognized_individualsobjectrequired

Performance metrics for a propensity model, optionally over a tenure range.

Properties of object

lift_curvearray[object]required

Items of array[object]

xnumberrequired

The quantile-based target percentile. For example, a value of .1 defines a target percentile specifying the top 10 percent of scores.

Example: 0.1

ynumberrequired

The estimated lift over random at the given target percentile.

Example: 0.75

y_maxnumberrequired

The upper error bound for y.

Example: 0.76

y_minnumberrequired

The lower error bound for y.

Example: 0.74

lift_tableobjectrequired

A table of arbitrary data. Purposefully untyped to allow for flexibility in the data.

Properties of object

headersarray[string]required

The headers of the table. Similar to a CSV header row.

rowsarray[array]required

The rows of the table. Similar to a CSV data row. Each row must have the same number of columns as the headers array. The order of the columns must match the order of the headers.

lift_valuenumber

Average of lift over random at average conversion rate (r), (0.75 * r), (0.5 * r), (0.25 * r).

Example: 1.425

metrics_tableobjectrequired

A table of arbitrary data. Purposefully untyped to allow for flexibility in the data.

Properties of object

headersarray[string]required

The headers of the table. Similar to a CSV header row.

rowsarray[array]required

The rows of the table. Similar to a CSV data row. Each row must have the same number of columns as the headers array. The order of the columns must match the order of the headers.

roc_aucnumberrequired

The area under the receiver operating characteristic curve.

Example: 0.75

roc_curvearray[object]required

Items of array[object]

xnumberrequired

A given false positive rate.

Example: 0.5

ynumberrequired

The estimated true positive rate corresponding to x.

Example: 0.75

strategy_idstring<uuid>

The ID of a strategy from the strategies array. If the group is overall, this value is null. To determine which strategies apply in such cases, refer to the strategy IDs defined at the tenure level.

Example: "b80217ae-7628-4c66-8a19-5bf452495c39"

tenure_endinteger

When the tenure range ends measured in days. The value can be null if no tenure range applies to this model. The value can also be null if the range is unbounded.

Example: 60

tenure_startinteger

When the tenure range starts measured in days. The value can be null if no tenure range applies to this model.

Example: 30

unrecognized_individualsobject

Performance metrics for a propensity model, optionally over a tenure range.

Properties of object

lift_curvearray[object]required

Items of array[object]

xnumberrequired

The quantile-based target percentile. For example, a value of .1 defines a target percentile specifying the top 10 percent of scores.

Example: 0.1

ynumberrequired

The estimated lift over random at the given target percentile.

Example: 0.75

y_maxnumberrequired

The upper error bound for y.

Example: 0.76

y_minnumberrequired

The lower error bound for y.

Example: 0.74

lift_tableobjectrequired

A table of arbitrary data. Purposefully untyped to allow for flexibility in the data.

Properties of object

headersarray[string]required

The headers of the table. Similar to a CSV header row.

rowsarray[array]required

The rows of the table. Similar to a CSV data row. Each row must have the same number of columns as the headers array. The order of the columns must match the order of the headers.

lift_valuenumber

Average of lift over random at average conversion rate (r), (0.75 * r), (0.5 * r), (0.25 * r).

Example: 1.425

metrics_tableobjectrequired

A table of arbitrary data. Purposefully untyped to allow for flexibility in the data.

Properties of object

headersarray[string]required

The headers of the table. Similar to a CSV header row.

rowsarray[array]required

The rows of the table. Similar to a CSV data row. Each row must have the same number of columns as the headers array. The order of the columns must match the order of the headers.

roc_aucnumberrequired

The area under the receiver operating characteristic curve.

Example: 0.75

roc_curvearray[object]required

Items of array[object]

xnumberrequired

A given false positive rate.

Example: 0.5

ynumberrequired

The estimated true positive rate corresponding to x.

Example: 0.75

strategy_idstring<uuid>

The ID of a strategy from the strategies array. If the group is overall, this value is null. To determine which strategies apply in such cases, refer to the strategy IDs defined at the tenure level.

Example: "b80217ae-7628-4c66-8a19-5bf452495c39"

tenure_endinteger

When the tenure range ends measured in days. The value can be null if no tenure range applies to this model. The value can also be null if the range is unbounded.

Example: 60

tenure_startinteger

When the tenure range starts measured in days. The value can be null if no tenure range applies to this model.

Example: 30

strategiesarray[object]

When an Outcome is being built, many "strategies" are employed to produce candidate models. After cross-validation, the best models resulting from, therefore, the best strategies are promoted to produ...

Items of array[object]

descriptionstringrequired

A description of the prediction strategy.

experimentalboolean

Whether the prediction strategy is experimental.

Example: false

idstring<uuid>required

A unique identifier for the prediction strategy. Referenced by strategy_id in the OutcomePerformanceModel in tenure performances.

namestringrequired

The name of the prediction strategy.

providersarray[string]required

The data providers used to make predictions.

"self" means uses first party data (your account).
"fig" means uses Faraday's Identity Graph.

slugstring

A unique identifier for the prediction strategy.

tenure_featuresobject

Properties of object

all_individualsarray[object]

Items of array[object]

featuresarray[object]required

Items of array[object]

directionalityarray[object]required

importancenumberrequired

The importance of the feature

literatestringrequired

The human-readable name of the feature

namestringrequired

The identifier for the feature

tenure_endinteger

When the tenure range ends measured in days. The value can be null if no tenure range applies. The value can also be null if the range is unbounded.

Example: 60

tenure_startinteger

When the tenure range starts measured in days. The value can be null if no tenure range applies.

Example: 30

recognized_individualsarray[object]required

Items of array[object]

featuresarray[object]required

Items of array[object]

directionalityarray[object]required

importancenumberrequired

The importance of the feature

literatestringrequired

The human-readable name of the feature

namestringrequired

The identifier for the feature

tenure_endinteger

When the tenure range ends measured in days. The value can be null if no tenure range applies. The value can also be null if the range is unbounded.

Example: 60

tenure_startinteger

When the tenure range starts measured in days. The value can be null if no tenure range applies.

Example: 30

unrecognized_individualsarray[object]

Items of array[object]

featuresarray[object]required

Items of array[object]

directionalityarray[object]required

importancenumberrequired

The importance of the feature

literatestringrequired

The human-readable name of the feature

namestringrequired

The identifier for the feature

tenure_endinteger

When the tenure range ends measured in days. The value can be null if no tenure range applies. The value can also be null if the range is unbounded.

Example: 60

tenure_startinteger

When the tenure range starts measured in days. The value can be null if no tenure range applies.

Example: 30

tenure_performancesobject

The performance metrics by data source, broken down further by tenure ranges, for an outcome.

Properties of object

all_individualsarray[object]

Items of array[object]

lift_curvearray[object]required

Items of array[object]

xnumberrequired

The quantile-based target percentile. For example, a value of .1 defines a target percentile specifying the top 10 percent of scores.

Example: 0.1

ynumberrequired

The estimated lift over random at the given target percentile.

Example: 0.75

y_maxnumberrequired

The upper error bound for y.

Example: 0.76

y_minnumberrequired

The lower error bound for y.

Example: 0.74

lift_tableobjectrequired

A table of arbitrary data. Purposefully untyped to allow for flexibility in the data.

Properties of object

headersarray[string]required

The headers of the table. Similar to a CSV header row.

rowsarray[array]required

The rows of the table. Similar to a CSV data row. Each row must have the same number of columns as the headers array. The order of the columns must match the order of the headers.

lift_valuenumber

Average of lift over random at average conversion rate (r), (0.75 * r), (0.5 * r), (0.25 * r).

Example: 1.425

metrics_tableobjectrequired

A table of arbitrary data. Purposefully untyped to allow for flexibility in the data.

Properties of object

headersarray[string]required

The headers of the table. Similar to a CSV header row.

rowsarray[array]required

The rows of the table. Similar to a CSV data row. Each row must have the same number of columns as the headers array. The order of the columns must match the order of the headers.

roc_aucnumberrequired

The area under the receiver operating characteristic curve.

Example: 0.75

roc_curvearray[object]required

Items of array[object]

xnumberrequired

A given false positive rate.

Example: 0.5

ynumberrequired

The estimated true positive rate corresponding to x.

Example: 0.75

strategy_idstring<uuid>

The ID of a strategy from the strategies array. If the group is overall, this value is null. To determine which strategies apply in such cases, refer to the strategy IDs defined at the tenure level.

Example: "b80217ae-7628-4c66-8a19-5bf452495c39"

tenure_endinteger

When the tenure range ends measured in days. The value can be null if no tenure range applies to this model. The value can also be null if the range is unbounded.

Example: 60

tenure_startinteger

When the tenure range starts measured in days. The value can be null if no tenure range applies to this model.

Example: 30

recognized_individualsarray[object]required

Items of array[object]

lift_curvearray[object]required

Items of array[object]

xnumberrequired

The quantile-based target percentile. For example, a value of .1 defines a target percentile specifying the top 10 percent of scores.

Example: 0.1

ynumberrequired

The estimated lift over random at the given target percentile.

Example: 0.75

y_maxnumberrequired

The upper error bound for y.

Example: 0.76

y_minnumberrequired

The lower error bound for y.

Example: 0.74

lift_tableobjectrequired

A table of arbitrary data. Purposefully untyped to allow for flexibility in the data.

Properties of object

headersarray[string]required

The headers of the table. Similar to a CSV header row.

rowsarray[array]required

The rows of the table. Similar to a CSV data row. Each row must have the same number of columns as the headers array. The order of the columns must match the order of the headers.

lift_valuenumber

Average of lift over random at average conversion rate (r), (0.75 * r), (0.5 * r), (0.25 * r).

Example: 1.425

metrics_tableobjectrequired

A table of arbitrary data. Purposefully untyped to allow for flexibility in the data.

Properties of object

headersarray[string]required

The headers of the table. Similar to a CSV header row.

rowsarray[array]required

The rows of the table. Similar to a CSV data row. Each row must have the same number of columns as the headers array. The order of the columns must match the order of the headers.

roc_aucnumberrequired

The area under the receiver operating characteristic curve.

Example: 0.75

roc_curvearray[object]required

Items of array[object]

xnumberrequired

A given false positive rate.

Example: 0.5

ynumberrequired

The estimated true positive rate corresponding to x.

Example: 0.75

strategy_idstring<uuid>

The ID of a strategy from the strategies array. If the group is overall, this value is null. To determine which strategies apply in such cases, refer to the strategy IDs defined at the tenure level.

Example: "b80217ae-7628-4c66-8a19-5bf452495c39"

tenure_endinteger

When the tenure range ends measured in days. The value can be null if no tenure range applies to this model. The value can also be null if the range is unbounded.

Example: 60

tenure_startinteger

When the tenure range starts measured in days. The value can be null if no tenure range applies to this model.

Example: 30

unrecognized_individualsarray[object]

Items of array[object]

lift_curvearray[object]required

Items of array[object]

xnumberrequired

The quantile-based target percentile. For example, a value of .1 defines a target percentile specifying the top 10 percent of scores.

Example: 0.1

ynumberrequired

The estimated lift over random at the given target percentile.

Example: 0.75

y_maxnumberrequired

The upper error bound for y.

Example: 0.76

y_minnumberrequired

The lower error bound for y.

Example: 0.74

lift_tableobjectrequired

A table of arbitrary data. Purposefully untyped to allow for flexibility in the data.

Properties of object

headersarray[string]required

The headers of the table. Similar to a CSV header row.

rowsarray[array]required

The rows of the table. Similar to a CSV data row. Each row must have the same number of columns as the headers array. The order of the columns must match the order of the headers.

lift_valuenumber

Average of lift over random at average conversion rate (r), (0.75 * r), (0.5 * r), (0.25 * r).

Example: 1.425

metrics_tableobjectrequired

A table of arbitrary data. Purposefully untyped to allow for flexibility in the data.

Properties of object

headersarray[string]required

The headers of the table. Similar to a CSV header row.

rowsarray[array]required

The rows of the table. Similar to a CSV data row. Each row must have the same number of columns as the headers array. The order of the columns must match the order of the headers.

roc_aucnumberrequired

The area under the receiver operating characteristic curve.

Example: 0.75

roc_curvearray[object]required

Items of array[object]

xnumberrequired

A given false positive rate.

Example: 0.5

ynumberrequired

The estimated true positive rate corresponding to x.

Example: 0.75

strategy_idstring<uuid>

The ID of a strategy from the strategies array. If the group is overall, this value is null. To determine which strategies apply in such cases, refer to the strategy IDs defined at the tenure level.

Example: "b80217ae-7628-4c66-8a19-5bf452495c39"

tenure_endinteger

When the tenure range ends measured in days. The value can be null if no tenure range applies to this model. The value can also be null if the range is unbounded.

Example: 60

tenure_startinteger

When the tenure range starts measured in days. The value can be null if no tenure range applies to this model.

Example: 30

400The request was invalid.

errorstringrequired

A Faraday error code.

Some possible values include:

Generic HTTP Errors

BAD_REQUEST: The request could not be validated.
FORBIDDEN: You do not have permission to access the specified resour...

Example: "ERROR_TYPE"

Allowed values:

BAD_REQUEST, FORBIDDEN, MAX_RESOURCES_REACHED, INTERNAL_SERVER_ERROR, INVALID_AUTHORIZATION, NOT_FOUND, MALFORMED_API_KEY, MISSING_API_KEY, EXPIRED_API_KEY, VALIDATION_FAILED, CONFLICT

idstring<uuid>required

A unique ID for this error. Please include this in bug reports.

Example: "082f9513-901c-4308-8081-902a8fe22d7e"

notestringrequired

A human-readable description of the error.

Example: "An error occurred"

validationErrorsarray[object]

JSON Schema validation errors, if any.

Items of array[object]

contextobjectrequired

More information about the error.

Properties of object

errorTypestringrequired

The type of validation error which occurred.

messagestringrequired

A human-readable error message.

pathstringrequired

The location in the document that failed validation.

suggestionstring

A suggestion for fixing this error.

401No API key was supplied.

errorstringrequired

A Faraday error code.

Some possible values include:

Generic HTTP Errors

BAD_REQUEST: The request could not be validated.
FORBIDDEN: You do not have permission to access the specified resour...

Example: "ERROR_TYPE"

Allowed values:

BAD_REQUEST, FORBIDDEN, MAX_RESOURCES_REACHED, INTERNAL_SERVER_ERROR, INVALID_AUTHORIZATION, NOT_FOUND, MALFORMED_API_KEY, MISSING_API_KEY, EXPIRED_API_KEY, VALIDATION_FAILED, CONFLICT

idstring<uuid>required

A unique ID for this error. Please include this in bug reports.

Example: "082f9513-901c-4308-8081-902a8fe22d7e"

notestringrequired

A human-readable description of the error.

Example: "An error occurred"

validationErrorsarray[object]

JSON Schema validation errors, if any.

Items of array[object]

contextobjectrequired

More information about the error.

Properties of object

errorTypestringrequired

The type of validation error which occurred.

messagestringrequired

A human-readable error message.

pathstringrequired

The location in the document that failed validation.

suggestionstring

A suggestion for fixing this error.

403Access to this resource was forbidden.

errorstringrequired

A Faraday error code.

Some possible values include:

Generic HTTP Errors

BAD_REQUEST: The request could not be validated.
FORBIDDEN: You do not have permission to access the specified resour...

Example: "ERROR_TYPE"

Allowed values:

BAD_REQUEST, FORBIDDEN, MAX_RESOURCES_REACHED, INTERNAL_SERVER_ERROR, INVALID_AUTHORIZATION, NOT_FOUND, MALFORMED_API_KEY, MISSING_API_KEY, EXPIRED_API_KEY, VALIDATION_FAILED, CONFLICT

idstring<uuid>required

A unique ID for this error. Please include this in bug reports.

Example: "082f9513-901c-4308-8081-902a8fe22d7e"

notestringrequired

A human-readable description of the error.

Example: "An error occurred"

validationErrorsarray[object]

JSON Schema validation errors, if any.

Items of array[object]

contextobjectrequired

More information about the error.

Properties of object

errorTypestringrequired

The type of validation error which occurred.

messagestringrequired

A human-readable error message.

pathstringrequired

The location in the document that failed validation.

suggestionstring

A suggestion for fixing this error.

404The requested resource ID was not found.

errorstringrequired

A Faraday error code.

Some possible values include:

Generic HTTP Errors

BAD_REQUEST: The request could not be validated.
FORBIDDEN: You do not have permission to access the specified resour...

Example: "ERROR_TYPE"

Allowed values:

BAD_REQUEST, FORBIDDEN, MAX_RESOURCES_REACHED, INTERNAL_SERVER_ERROR, INVALID_AUTHORIZATION, NOT_FOUND, MALFORMED_API_KEY, MISSING_API_KEY, EXPIRED_API_KEY, VALIDATION_FAILED, CONFLICT

idstring<uuid>required

A unique ID for this error. Please include this in bug reports.

Example: "082f9513-901c-4308-8081-902a8fe22d7e"

notestringrequired

A human-readable description of the error.

Example: "An error occurred"

validationErrorsarray[object]

JSON Schema validation errors, if any.

Items of array[object]

contextobjectrequired

More information about the error.

Properties of object

errorTypestringrequired

The type of validation error which occurred.

messagestringrequired

A human-readable error message.

pathstringrequired

The location in the document that failed validation.

suggestionstring

A suggestion for fixing this error.

500An internal server error occurred.

errorstringrequired

A Faraday error code.

Some possible values include:

Generic HTTP Errors

BAD_REQUEST: The request could not be validated.
FORBIDDEN: You do not have permission to access the specified resour...

Example: "ERROR_TYPE"

Allowed values:

BAD_REQUEST, FORBIDDEN, MAX_RESOURCES_REACHED, INTERNAL_SERVER_ERROR, INVALID_AUTHORIZATION, NOT_FOUND, MALFORMED_API_KEY, MISSING_API_KEY, EXPIRED_API_KEY, VALIDATION_FAILED, CONFLICT

idstring<uuid>required

A unique ID for this error. Please include this in bug reports.

Example: "082f9513-901c-4308-8081-902a8fe22d7e"

notestringrequired

A human-readable description of the error.

Example: "An error occurred"

validationErrorsarray[object]

JSON Schema validation errors, if any.

Items of array[object]

contextobjectrequired

More information about the error.

Properties of object

errorTypestringrequired

The type of validation error which occurred.

messagestringrequired

A human-readable error message.

pathstringrequired

The location in the document that failed validation.

suggestionstring

A suggestion for fixing this error.

Tags

outcomes

Test request

Request snippet

curl -X GET 'https://api.faraday.ai/v1/outcomes/{outcome_id}/analysis' \
  -H 'Content-Type: application/json' \
  -H 'Authorization: Bearer YOUR_API_KEY'

Example response

application/json

{
  "bias": {
    "data": {
      "age": {
        "candidates": [
          {
            "x": 0,
            "y": 0
          }
        ],
        "level": "low_bias",
        "negatives": [
          {
            "x": 0,
            "y": 0
          }
        ],
        "positives": [
          {
            "x": 0,
            "y": 0
          }
        ]
      },
      "gender": {
        "candidates": [
          {
            "x": "string",
            "y": 0
          }
        ],
        "level": "low_bias",
        "negatives": [
          {
            "x": "string",
            "y": 0
          }
        ],
        "positives": [
          {
            "x": "string",
            "y": 0
          }
        ]
      }
    },
    "fairness": [
      {
        "dimensions": [
          "string"
        ],
        "metrics": [
          {
            "level": "string",
            "name": "string",
            "value": 0
          }
        ],
        "values": [
          "string"
        ]
      }
    ],
    "power": [
      {
        "dimensions": [
          "string"
        ],
        "metrics": [
          {
            "level": "string",
            "name": "string",
            "value": 0
          }
        ],
        "values": [
          "string"
        ]
      }
    ],
    "predictions": [
      {
        "dimensions": [
          "string"
        ],
        "metrics": [
          {
            "level": "string",
            "name": "string",
            "value": 0
          }
        ],
        "values": [
          "string"
        ]
      }
    ],
    "summary": {
      "data_unbiased": true,
      "fairness_fair": true,
      "power_unbiased": true,
      "predictions_unbiased": true
    }
  },
  "overall_features": {
    "all_individuals": [
      {
        "directionality": [
          {
            "data_type": "boolean",
            "density": 0.75,
            "impact": 0,
            "value": true
          }
        ],
        "importance": 0,
        "literate": "string",
        "name": "string"
      }
    ],
    "recognized_individuals": [
      {
        "directionality": [
          {
            "data_type": "boolean",
            "density": 0.75,
            "impact": 0,
            "value": true
          }
        ],
        "importance": 0,
        "literate": "string",
        "name": "string"
      }
    ],
    "unrecognized_individuals": [
      {
        "directionality": [
          {
            "data_type": "boolean",
            "density": 0.75,
            "impact": 0,
            "value": true
          }
        ],
        "importance": 0,
        "literate": "string",
        "name": "string"
      }
    ]
  },
  "overall_performance": {
    "all_individuals": {
      "lift_curve": [
        {
          "x": 0.1,
          "y": 0.75,
          "y_max": 0.76,
          "y_min": 0.74
        }
      ],
      "lift_table": {
        "headers": [
          "string"
        ],
        "rows": [
          [
            "string"
          ]
        ]
      },
      "lift_value": 1.425,
      "metrics_table": {
        "headers": [
          "string"
        ],
        "rows": [
          [
            "string"
          ]
        ]
      },
      "roc_auc": 0.75,
      "roc_curve": [
        {
          "x": 0.5,
          "y": 0.75
        }
      ],
      "strategy_id": "b80217ae-7628-4c66-8a19-5bf452495c39",
      "tenure_end": 60,
      "tenure_start": 30
    },
    "recognized_individuals": {
      "lift_curve": [
        {
          "x": 0.1,
          "y": 0.75,
          "y_max": 0.76,
          "y_min": 0.74
        }
      ],
      "lift_table": {
        "headers": [
          "string"
        ],
        "rows": [
          [
            "string"
          ]
        ]
      },
      "lift_value": 1.425,
      "metrics_table": {
        "headers": [
          "string"
        ],
        "rows": [
          [
            "string"
          ]
        ]
      },
      "roc_auc": 0.75,
      "roc_curve": [
        {
          "x": 0.5,
          "y": 0.75
        }
      ],
      "strategy_id": "b80217ae-7628-4c66-8a19-5bf452495c39",
      "tenure_end": 60,
      "tenure_start": 30
    },
    "unrecognized_individuals": {
      "lift_curve": [
        {
          "x": 0.1,
          "y": 0.75,
          "y_max": 0.76,
          "y_min": 0.74
        }
      ],
      "lift_table": {
        "headers": [
          "string"
        ],
        "rows": [
          [
            "string"
          ]
        ]
      },
      "lift_value": 1.425,
      "metrics_table": {
        "headers": [
          "string"
        ],
        "rows": [
          [
            "string"
          ]
        ]
      },
      "roc_auc": 0.75,
      "roc_curve": [
        {
          "x": 0.5,
          "y": 0.75
        }
      ],
      "strategy_id": "b80217ae-7628-4c66-8a19-5bf452495c39",
      "tenure_end": 60,
      "tenure_start": 30
    }
  },
  "strategies": [
    {
      "description": "string",
      "experimental": false,
      "id": "123e4567-e89b-12d3-a456-426614174000",
      "name": "string",
      "providers": [
        "self"
      ],
      "slug": "string"
    }
  ],
  "tenure_features": {
    "all_individuals": [
      {
        "features": [
          {
            "directionality": [
              {
                "data_type": "boolean",
                "density": 0.75,
                "impact": 0,
                "value": true
              }
            ],
            "importance": 0,
            "literate": "string",
            "name": "string"
          }
        ],
        "tenure_end": 60,
        "tenure_start": 30
      }
    ],
    "recognized_individuals": [
      {
        "features": [
          {
            "directionality": [
              {
                "data_type": "boolean",
                "density": 0.75,
                "impact": 0,
                "value": true
              }
            ],
            "importance": 0,
            "literate": "string",
            "name": "string"
          }
        ],
        "tenure_end": 60,
        "tenure_start": 30
      }
    ],
    "unrecognized_individuals": [
      {
        "features": [
          {
            "directionality": [
              {
                "data_type": "boolean",
                "density": 0.75,
                "impact": 0,
                "value": true
              }
            ],
            "importance": 0,
            "literate": "string",
            "name": "string"
          }
        ],
        "tenure_end": 60,
        "tenure_start": 30
      }
    ]
  },
  "tenure_performances": {
    "all_individuals": [
      {
        "lift_curve": [
          {
            "x": 0.1,
            "y": 0.75,
            "y_max": 0.76,
            "y_min": 0.74
          }
        ],
        "lift_table": {
          "headers": [
            "string"
          ],
          "rows": [
            [
              "string"
            ]
          ]
        },
        "lift_value": 1.425,
        "metrics_table": {
          "headers": [
            "string"
          ],
          "rows": [
            [
              "string"
            ]
          ]
        },
        "roc_auc": 0.75,
        "roc_curve": [
          {
            "x": 0.5,
            "y": 0.75
          }
        ],
        "strategy_id": "b80217ae-7628-4c66-8a19-5bf452495c39",
        "tenure_end": 60,
        "tenure_start": 30
      }
    ],
    "recognized_individuals": [
      {
        "lift_curve": [
          {
            "x": 0.1,
            "y": 0.75,
            "y_max": 0.76,
            "y_min": 0.74
          }
        ],
        "lift_table": {
          "headers": [
            "string"
          ],
          "rows": [
            [
              "string"
            ]
          ]
        },
        "lift_value": 1.425,
        "metrics_table": {
          "headers": [
            "string"
          ],
          "rows": [
            [
              "string"
            ]
          ]
        },
        "roc_auc": 0.75,
        "roc_curve": [
          {
            "x": 0.5,
            "y": 0.75
          }
        ],
        "strategy_id": "b80217ae-7628-4c66-8a19-5bf452495c39",
        "tenure_end": 60,
        "tenure_start": 30
      }
    ],
    "unrecognized_individuals": [
      {
        "lift_curve": [
          {
            "x": 0.1,
            "y": 0.75,
            "y_max": 0.76,
            "y_min": 0.74
          }
        ],
        "lift_table": {
          "headers": [
            "string"
          ],
          "rows": [
            [
              "string"
            ]
          ]
        },
        "lift_value": 1.425,
        "metrics_table": {
          "headers": [
            "string"
          ],
          "rows": [
            [
              "string"
            ]
          ]
        },
        "roc_auc": 0.75,
        "roc_curve": [
          {
            "x": 0.5,
            "y": 0.75
          }
        ],
        "strategy_id": "b80217ae-7628-4c66-8a19-5bf452495c39",
        "tenure_end": 60,
        "tenure_start": 30
      }
    ]
  }
}