test_classification_gpu
Evaluate a pretrained classification model on custom test 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
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
Column containing the model predictions.
Column containing the model prediction probabilities.
Column containing the model prediction errors.
Optional column identifying the train/test split, if dataset was randomly sampled and model re-fit.
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).
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.
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.