API Reference - PerformanceReport

Performance Report¶

Imports¶

[1]:

from deepchecks.base import Dataset
import matplotlib.pyplot as plt
from sklearn.ensemble import AdaBoostClassifier, AdaBoostRegressor
from sklearn.datasets import load_iris
import pandas as pd
from sklearn.model_selection import train_test_split
from deepchecks.checks.performance import PerformanceReport

Generating data:¶

[2]:

iris = load_iris(as_frame=True)
clf = AdaBoostClassifier()
frame = iris.frame
X = iris.data
Y = iris.target
X_train, X_test, y_train, y_test = train_test_split(
            X, Y, test_size=0.33, random_state=42)
train_ds = Dataset(pd.concat([X_train, y_train], axis=1),
            features=iris.feature_names,
            label='target')
test_ds = Dataset(pd.concat([X_test, y_test], axis=1),
            features=iris.feature_names,
            label='target')
_ = clf.fit(X_train, y_train)

Running peformance report on classification¶

[3]:

check = PerformanceReport()
check.run(train_ds, test_ds, clf)

Performance Report

Summarize given scores on a dataset and model. Read More...

Additional Outputs

Generate regression data¶

[4]:

from sklearn.datasets import load_diabetes

diabetes = load_diabetes(as_frame=True)
clf = AdaBoostRegressor()

frame = diabetes.frame
X = diabetes.data
Y = diabetes.target
X_train, X_test, y_train, y_test = train_test_split(
            X, Y, test_size=0.33, random_state=42)
train_ds = Dataset(pd.concat([X_train, y_train], axis=1),
            features=diabetes.feature_names,
            label='target', cat_features=['sex'])
test_ds = Dataset(pd.concat([X_test, y_test], axis=1),
            features=diabetes.feature_names,
            label='target', cat_features=['sex'])
_ = clf.fit(X_train, y_train)

Run performance report on regression¶

[5]:

check = PerformanceReport()
check.run(train_ds, test_ds, clf)