Tabular Data Classification on Income Dataset¶
In this demo we will:
Create a pipeline which can be used to classify tabular data
Create an explainer
Send a request to a prediction pipeline
View the explanation
Create a pipeline¶
1. Click on Create new deployment¶
2. Name your pipeline¶
In this demo we’re naming the pipeline income
You also need to specify the type as Seldon ML Pipeline
3. Select model runtime¶
The example model is a scikit-learn model, so we’ll select scikit-learn
as the runtime
4. Provide model URI¶
In this demo we’re using this model URI: gs://seldon-models/scv2/examples/mlserver_1.2.3/income/classifier
Create an explainer¶
1. Select your pipeline¶
2. Navigate to the explainer section¶
There you’ll find the add button which will allow you to create an explainer for your pipeline.
3. Specify your model data type¶
This model is trained to classify tabular data, so we selected Tabular
4. Provide explainer URI¶
Explainer model URI gs://seldon-models/scv2/examples/mlserver_1.2.3/income/explainer
Make a prediction¶
1. Select your pipeline¶
2. Click on Predict¶
3. Provide prediction data¶
For this example please use this payload
{
"parameters": {"content_type": "pd"},
"inputs": [
{"name": "Age", "shape": [1, 1], "datatype": "INT64", "data": [47]},
{"name": "Workclass", "shape": [1, 1], "datatype": "INT64", "data": [4]},
{"name": "Education", "shape": [1, 1], "datatype": "INT64", "data": [1]},
{"name": "Marital Status", "shape": [1, 1], "datatype": "INT64", "data": [1]},
{"name": "Occupation", "shape": [1, 1], "datatype": "INT64", "data": [1]},
{"name": "Relationship", "shape": [1, 1], "datatype": "INT64", "data": [3]},
{"name": "Race", "shape": [1, 1], "datatype": "INT64", "data": [4]},
{"name": "Sex", "shape": [1, 1], "datatype": "INT64", "data": [1]},
{"name": "Capital Gain", "shape": [1, 1], "datatype": "INT64", "data": [0]},
{"name": "Capital Loss", "shape": [1, 1], "datatype": "INT64", "data": [0]},
{"name": "Hours per week", "shape": [1, 1], "datatype": "INT64", "data": [40]},
{"name": "Country", "shape": [1, 1], "datatype": "INT64", "data": [9]}
]
}
4. View the response¶
View the explanation¶
1. Select your pipeline¶
2. Click on Requests¶
3. Find the request you made¶
To view the explanation click on the View explanation button.
4. View the explanation¶
Here you can see the explanation for the prediction we made earlier.