test_regression
Evaluate a pretrained regression model on custom test data.
Usually employed after the
train_regression 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
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:
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
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
Target variable.
Name of the column that contains your target values (labels).
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.
Options
Options
Size of test split.
The fraction of data used for testing. The remaining data will be used to refit the model.Values must be in the following range:
Random seed.
Seed used to initialize the random number generator assigning rows to train/test splits.
If none is provided, result will be non-deterministic.
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.