#Let the random-forest model be: *rf_model*
from kale.common.serveutils import serve
kfserver = serve(rf_model) #model is now being deployed
#prepare data for prediction
data = [row.tolist() for _, row in
train_df[predictor_var].head(10).iterrows()]
data_json = json.dumps({"instances": data})
#prediciton using deployed model:
pred = kfserver.predict(data_json)
Question 1: The returned pred is class labels: 0/1. How to return probabilities?
I tried the following way after studying: kale.common.serveutils.predict
#let HOST be the host name of deployed model
#let the URL of calling the deployed model be: http://xxx:predict
headers = {"content-type": "application/json", "Host": HOST}
pred_2 = requests.post(url = URL, data=data_json, headers=headers)
Question 2: But not clear, where to set parameter, so pred_2 will return probabilities?
