predict_classification
Use a pretrained classification model to predict new categorical data.
Usually employed after the train_classification step.
???+ info “Prediction Model”
To use this step successfully you need to make sure the dataset you’re predicting on is
as similar as possible to the one the model was trained on. We check that the necessary data
types and columns are present, but you should pay attention to how you handled these in the
recipe the model was generated. Any changes might lead to a significant degradation in
model performance.
Usage
The following examples show how the step can be used in a recipe.
Examples
Examples
To only predict the label of the class with the highest probability
Inputs & Outputs
The following are the inputs expected by the step and the outputs it produces. These are generally
columns (ds.first_name), datasets (ds or ds[["first_name", "last_name"]]) or models (referenced
by name e.g. "churn-clf").
Inputs
Inputs
Outputs
Outputs
Column(s) containing the model predictions. If a single output column is provided, the model will output the predicted class. If two column names are provided, the model will additionally output the predicted probabilities.
Configuration
The following parameters can be used to configure the behaviour of the step by including them in
a json object as the last “input” to the step, i.e. step(..., {"param": "value", ...}) -> (output).
Parameters
Parameters
Name of the positive class. In binary classification, usually the class you’re most interested in, for example the label/class corresponding to successful lead conversion in a lead score model, the class corresponding to a customer who has churned in a churn prediction model, etc.
If provided, will return predicted probabilities for the positive class. If not provided, will return probabilities for the predicted class (i.e. the ‘winning’ class with the highest probability).