test_classification
Evaluate a pretrained classification model on custom test data.
Usually employed after the train_classification step. Useful to potentially
refit the model on a single dataset split and/or predict samples the model
hasn’t seen before, calculate errors etc., i.e. for sample-based analysis
of model performance.
Usage
The following examples show how the step can be used in a recipe.
Assuming we have reserved a test set containing data that wasn’t used to train the model, we can simply pass it to this step for evaluation:
Assuming we have reserved a test set containing data that wasn’t used to train the model, we can simply pass it to this step for evaluation:
If the test data is contained in a larger dataset (e.g. along training data), but can be identified using a column indicating the split, we can use the following setup:
Alternatively, we can create a randomized train/test split on the fly, re-fit the model on the train set, and evaluate on the test set. In this case an additional column will be added to the dataset, indicating the split each row belongs to:
General syntax for using the step in a recipe. Shows the inputs and outputs the step is expected to receive and will produce respectively. For futher details see sections below.
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").
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).
Target variable. Name of the column that contains your target values (labels).
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 class with the highest probability).
Train/test split configuration. Identify the splits using an existing column or create a randomized split. In either case, the model will be refit on the train split and evaluated on the test split.
Whether to retrain the model.
If set to true, the model will be refit on the train split before evaluation. If set to false,
the model will be evaluated on the test split without refitting. If no split configuration is provided,
this parameter is ignored.
Was this page helpful?