• API Reference - MultiModelPerformanceReport

Multi Model Performance Report

This notebook provides an overview for using and understanding the Multi Model Performance Report check.

Structure:

• What is the Multi Model Performance Report check?

• Multiclass check

• Regression check

What is the Multi Model Performance Report?

The MultiModelPerformanceReport check produces a summary of performance scores for multiple models on test datasets. The default scorers that are used are F1, Precision and Recall for Classification and Negative RMSE (Root Mean Square Error), Negative MAE (Mean Absolute Error) and R2 for Regression.

Multiclass check

Imports

from sklearn.datasets import load_iris
from sklearn.ensemble import AdaBoostClassifier, RandomForestClassifier
from sklearn.model_selection import train_test_split
from sklearn.tree import DecisionTreeClassifier

from deepchecks.tabular import Dataset
from deepchecks.tabular.checks import MultiModelPerformanceReport

Generate data & model

iris = load_iris(as_frame=True)
train, test = train_test_split(iris.frame, test_size=0.33, random_state=42)

train_ds = Dataset(train, label="target")
test_ds = Dataset(test, label="target")

features = train_ds.data[train_ds.features]
label = train_ds.data[train_ds.label_name]
clf1 = AdaBoostClassifier().fit(features, label)
clf2 = RandomForestClassifier().fit(features, label)
clf3 = DecisionTreeClassifier().fit(features, label)

Run the check

MultiModelPerformanceReport().run(train_ds, test_ds, [clf1, clf2, clf3])

Regression check

Imports

from sklearn.datasets import load_diabetes
from sklearn.ensemble import AdaBoostRegressor, RandomForestRegressor
from sklearn.tree import DecisionTreeRegressor

Generate data & model

diabetes = load_diabetes(as_frame=True)
train, test = train_test_split(diabetes.frame, test_size=0.33, random_state=42)

train_ds = Dataset(train, label="target", cat_features=['sex'])
test_ds = Dataset(test, label="target", cat_features=['sex'])

features = train_ds.data[train_ds.features]
label = train_ds.data[train_ds.label_name]
clf1 = AdaBoostRegressor().fit(features, label)
clf2 = RandomForestRegressor().fit(features, label)
clf3 = DecisionTreeRegressor().fit(features, label)

Run the check

MultiModelPerformanceReport().run(train_ds, test_ds, [clf1, clf2, clf3])

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Model Info

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Regression Error Distribution