Multi Model Performance Report#

Multiclass#

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

Multi Model Performance Report

Summarize performance scores for multiple models on test datasets.

Additional Outputs


Regression#

from sklearn.datasets import load_diabetes
from sklearn.ensemble import AdaBoostRegressor, RandomForestRegressor
from sklearn.tree import DecisionTreeRegressor
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)
MultiModelPerformanceReport().run(train_ds, test_ds, [clf1, clf2, clf3])

Multi Model Performance Report

Summarize performance scores for multiple models on test datasets.

Additional Outputs


Total running time of the script: ( 0 minutes 0.902 seconds)

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