• API Reference - SimpleModelComparison

Simple Model Comparison

This notebook provides an overview for using and understanding simple model comparison check.

Structure:

• What is the purpose of the check?

• Generate data & model

• Run the check

• Define a condition

What is the purpose of the check?

The simple model is designed to produce the best performance achievable using very simple rules. The goal of the simple model is to provide a baseline of minimal model performance for the given task, to which the user model may be compared. If the user model achieves less or a similar score to the simple model, this is an indicator for a possible problem with the model (e.g. it wasn’t trained properly).

The check has three possible “simple model” heuristics, from which one is chosen and compared to. By default the check uses the most_frequent heuristic, which can be overridden in the checks’ parameters using strategy. There is no simple model which is more “correct” to use, each gives a different baseline to compare to, and you may experiment with the different types and see how it performs on your data.

The simple models are:

• A most_frequent model - The default. In regression the prediction is equal to the mean value, in classification the prediction is equal to the most common value.

• A uniform model - In regression, selects a random value from the y range. In classification, selects one of the labels by random.

• A stratified model - Draws the prediction from the distribution of the labels in the train.

• A tree model - Trains a simple decision tree with a given depth. The depth can be customized using the max_depth parameter.

Generate data & model

from deepchecks.tabular.datasets.classification.phishing import (
    load_data, load_fitted_model)

train_dataset, test_dataset = load_data()
model = load_fitted_model()

Run the check

We will run the check with the tree model type. The check will use the default metric defined in deepchecks for classification. This can be overridden by providing an alternative scorer using the alternative_scorers parameter.

Note that we are demonstrating on a classification task, but the check also works for regression tasks. For classification we will see the metrics per class, and for regression we’ll have a single result per metric.

from deepchecks.tabular.checks import SimpleModelComparison

# Using tree model as a simple model, and changing the tree depth from the default 3 to 5
check = SimpleModelComparison(strategy='tree', max_depth=5)
check.run(train_dataset, test_dataset, model)

Observe the check’s output

We can see in the results that the check calculates the score for each class in the dataset, and compares the scores between our model and the simple model. In addition to the graphic output, the check also returns a value which includes all of the information that is needed for defining the conditions for validation.

The value is a dictionary of:

• scores - for each metric and class returns the numeric score

• type - the model task type

• scorers_perfect - for each metric the perfect possible score (used to calculate gain)

• classes - the classes exists in the data

Note: for regression scores will contain for each metric a single numeric score, and classes will be null.

check = SimpleModelComparison()
result = check.run(train_dataset, test_dataset, model)
result.value

{'scores': {'F1': defaultdict(<class 'dict'>, {0: {'Origin': 0.9965991902834008, 'Simple': 0.9843400447427293}, 1: {'Origin': 0.8839779005524863, 'Simple': 0.0}})}, 'type': <TaskType.BINARY: 'binary'>, 'scorers_perfect': {'F1': 1.0}}

Define a condition

We can define on our check a condition that will validate our model is better than the simple model by a given margin called gain. For classification we check the gain for each class separately and if there is a class that doesn’t pass the defined gain the condition will fail.

The performance gain is the percent of the improved performance out of the “remaining” unattained performance. Its purpose is to reflect the significance of the said improvement. Take for example for a metric between 0 and 1. A change of only 0.03 that takes us from 0.95 to 0.98 is highly significant (especially in an imbalance scenario), but improving from 0.1 to 0.13 is not a great achievement.

The gain is calculated as:

Let’s add a condition to the check and see what happens when it fails:

check = SimpleModelComparison(strategy='tree')
check.add_condition_gain_greater_than(0.9)
result = check.run(train_dataset, test_dataset, model)
result.show(show_additional_outputs=False)

We detected that for class “1” our gain did not passed the target gain we defined, therefore it failed.

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