Multi Model Performance Report¶
Multiclass¶
[1]:
from deepchecks.base import Dataset
from sklearn.ensemble import AdaBoostClassifier, RandomForestClassifier
from sklearn.tree import DecisionTreeClassifier
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from deepchecks.checks.performance import MultiModelPerformanceReport
[2]:
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)
[3]:
MultiModelPerformanceReport().run(train_ds, test_ds, [clf1, clf2, clf3])
Multi Model Performance Report
Summarize performance scores for multiple models on test datasets. Read More...
Additional Outputs
Regression¶
[4]:
from sklearn.datasets import load_diabetes
from sklearn.ensemble import AdaBoostRegressor, RandomForestRegressor
from sklearn.tree import DecisionTreeRegressor
[5]:
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)
[6]:
MultiModelPerformanceReport().run(train_ds, test_ds, [clf1, clf2, clf3])
Multi Model Performance Report
Summarize performance scores for multiple models on test datasets. Read More...