{"library":"sklearn-evaluation","title":"sklearn-evaluation","description":"A Python library for evaluating scikit-learn models, providing a rich set of plots, tables, and markdown reports. Current version 0.12.2. Released irregularly; latest releases are minor patches.","language":"python","status":"active","last_verified":"Fri May 01","install":{"commands":["pip install sklearn-evaluation"],"cli":null},"imports":["from sklearn_evaluation import ClassificationReport"],"auth":{"required":false,"env_vars":[]},"quickstart":{"code":"from sklearn.datasets import make_classification\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.ensemble import RandomForestClassifier\nfrom sklearn_evaluation import ClassificationReport\n\nX, y = make_classification(random_state=0)\nX_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0)\nclf = RandomForestClassifier(random_state=0)\nclf.fit(X_train, y_train)\ny_pred = clf.predict(X_test)\n\nreport = ClassificationReport(y_test, y_pred)\nprint(report)\nreport.plot()","lang":"python","description":"Train a Random Forest classifier and generate a classification report.","tag":null,"tag_description":null,"last_tested":null,"results":[]},"compatibility":null}