{"library":"mlxtend","title":"Machine Learning Library Extensions (mlxtend)","description":"mlxtend is a Python library of useful tools for machine learning tasks, offering a diverse range of functionality including feature selection, frequent pattern mining, model stacking, and plotting utilities. It builds upon popular libraries like scikit-learn, NumPy, and pandas. The current version is 0.24.0, and it maintains an active release cadence, frequently pushing updates for bug fixes and compatibility with its core dependencies.","language":"python","status":"active","last_verified":"Fri May 15","install":{"commands":["pip install mlxtend"],"cli":null},"imports":["from mlxtend.classifier import StackingClassifier","from mlxtend.feature_selection import SequentialFeatureSelector","from mlxtend.plotting import plot_decision_regions","from mlxtend.preprocessing import TransactionEncoder","from mlxtend.frequent_patterns import apriori","from mlxtend.frequent_patterns import association_rules"],"auth":{"required":false,"env_vars":[]},"quickstart":{"code":"import numpy as np\nfrom sklearn.linear_model import LogisticRegression\nfrom sklearn.tree import DecisionTreeClassifier\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.datasets import make_classification\nfrom mlxtend.classifier import StackingClassifier\n\n# Generate a synthetic dataset\nX, y = make_classification(n_samples=1000, n_features=20, n_informative=10, n_redundant=10, random_state=42)\nX_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)\n\n# Initialize base classifiers\nclf1 = DecisionTreeClassifier(random_state=42)\nclf2 = LogisticRegression(random_state=42, solver='liblinear')\n\n# Initialize meta-classifier\nlr = LogisticRegression(random_state=42, solver='liblinear')\n\n# Initialize StackingClassifier\nsclf = StackingClassifier(classifiers=[clf1, clf2], meta_classifier=lr, use_probas=True, verbose=0)\n\n# Train and evaluate\nsclf.fit(X_train, y_train)\nscore = sclf.score(X_test, y_test)\nprint(f\"Stacking Classifier Test Accuracy: {score:.4f}\")","lang":"python","description":"This quickstart demonstrates how to use the `StackingClassifier` to combine multiple base models (Decision Tree, Logistic Regression) with a meta-classifier (Logistic Regression) to improve prediction accuracy. It uses a synthetic dataset from scikit-learn for illustration.","tag":null,"tag_description":null,"last_tested":null,"results":[]},"compatibility":{"tag":null,"tag_description":null,"last_tested":"2026-05-15","installed_version":"0.23.4","pypi_latest":"0.24.0","is_stale":true,"summary":{"python_range":"3.10–3.9","success_rate":50,"avg_install_s":17.3,"avg_import_s":4.49,"wheel_type":"wheel"},"results":[{"runtime":"python:3.10-alpine","python_version":"3.10","os_libc":"alpine (musl)","variant":"mlxtend","exit_code":1,"wheel_type":null,"failure_reason":"build_error","import_side_effects":null,"install_time_s":null,"import_time_s":null,"mem_mb":null,"disk_size":null},{"runtime":"python:3.10-slim","python_version":"3.10","os_libc":"slim (glibc)","variant":"mlxtend","exit_code":0,"wheel_type":"wheel","failure_reason":null,"import_side_effects":"clean","install_time_s":16.8,"import_time_s":2.95,"mem_mb":63.4,"disk_size":"428M"},{"runtime":"python:3.11-alpine","python_version":"3.11","os_libc":"alpine (musl)","variant":"mlxtend","exit_code":1,"wheel_type":null,"failure_reason":"build_error","import_side_effects":null,"install_time_s":null,"import_time_s":null,"mem_mb":null,"disk_size":null},{"runtime":"python:3.11-slim","python_version":"3.11","os_libc":"slim (glibc)","variant":"mlxtend","exit_code":0,"wheel_type":"wheel","failure_reason":null,"import_side_effects":"clean","install_time_s":15.9,"import_time_s":5.03,"mem_mb":77.1,"disk_size":"457M"},{"runtime":"python:3.12-alpine","python_version":"3.12","os_libc":"alpine (musl)","variant":"mlxtend","exit_code":1,"wheel_type":null,"failure_reason":"build_error","import_side_effects":null,"install_time_s":null,"import_time_s":null,"mem_mb":null,"disk_size":null},{"runtime":"python:3.12-slim","python_version":"3.12","os_libc":"slim (glibc)","variant":"mlxtend","exit_code":0,"wheel_type":"wheel","failure_reason":null,"import_side_effects":"clean","install_time_s":16.9,"import_time_s":6.01,"mem_mb":75.5,"disk_size":"436M"},{"runtime":"python:3.13-alpine","python_version":"3.13","os_libc":"alpine (musl)","variant":"mlxtend","exit_code":1,"wheel_type":null,"failure_reason":"build_error","import_side_effects":null,"install_time_s":null,"import_time_s":null,"mem_mb":null,"disk_size":null},{"runtime":"python:3.13-slim","python_version":"3.13","os_libc":"slim (glibc)","variant":"mlxtend","exit_code":0,"wheel_type":"wheel","failure_reason":null,"import_side_effects":"clean","install_time_s":16.9,"import_time_s":5.45,"mem_mb":74.7,"disk_size":"434M"},{"runtime":"python:3.9-alpine","python_version":"3.9","os_libc":"alpine (musl)","variant":"mlxtend","exit_code":1,"wheel_type":null,"failure_reason":"build_error","import_side_effects":null,"install_time_s":null,"import_time_s":null,"mem_mb":null,"disk_size":null},{"runtime":"python:3.9-slim","python_version":"3.9","os_libc":"slim (glibc)","variant":"mlxtend","exit_code":0,"wheel_type":"wheel","failure_reason":null,"import_side_effects":"clean","install_time_s":20,"import_time_s":3.03,"mem_mb":60.2,"disk_size":"439M"}]}}