Random Forest Algorithm

Random Forest Algorithm 是一种功能强大的 Ensemble Learning 方法,它通过在训练时构建大量决策树,并输出作为单个决策树类别模式的类别。Coursera 的 Random Forest 算法目录将向您介绍这种用于机器学习和数据挖掘的通用算法的来龙去脉。您将学习如何在分类和 Regression 任务中实施这种算法,了解特征的重要性,处理缺失值,并调整 Hyperparameter 以获得最佳性能。掌握这项技能后,您就能高效地处理大型数据集,解决医疗保健、银行和电子商务等各个领域的复杂预测问题。
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“random forest algorithm” 的结果

  • 状态:免费试用

    University of Colorado Boulder

    您将获得的技能: 应用数学, 数据清理, 机器学习, 数据科学, Matplotlib, 预测建模, 探索性数据分析, 监督学习, 回归分析, 分类与回归树 (CART), 随机森林算法, 机器学习算法, Python 程序设计, 数学建模, 决策树学习, 统计编程, 应用机器学习, 功能工程, 性能调整, Scikit-learn (机器学习库)

  • 状态:免费试用

    您将获得的技能: 数据收集, 合规管理, MLOps(机器学习 Operator), 数据分析, 数据伦理, 回归分析, 无监督学习, 生产率, 设定目标, 随机森林算法, 工作流程管理, 学习策略, 机器学习算法, 商业道德, 负责任的人工智能, 应用机器学习, 测试计划, 统计分析, 深度学习, 决策树学习

  • 状态:免费试用

    您将获得的技能: Supervised Learning, Data Modeling, Unsupervised Learning, Applied Machine Learning, Data Analysis, Reinforcement Learning, Artificial Intelligence, Classification And Regression Tree (CART), Tensorflow, Machine Learning Algorithms, Keras (Neural Network Library), Artificial Neural Networks, Deep Learning, Predictive Modeling, Machine Learning, Regression Analysis, Data Ethics, Responsible AI, Artificial Intelligence and Machine Learning (AI/ML), Random Forest Algorithm

  • 您将获得的技能: Feature Engineering, Classification And Regression Tree (CART), Decision Tree Learning, Applied Machine Learning, Random Forest Algorithm, Responsible AI, Predictive Modeling, Data Import/Export, Machine Learning, Predictive Analytics

  • 状态:预览

    您将获得的技能: Supervised Learning, Decision Tree Learning, Applied Machine Learning, Data Processing, Predictive Modeling, Statistical Machine Learning, Random Forest Algorithm, Feature Engineering, SAS (Software), Machine Learning, Data Analysis, Artificial Neural Networks, Data Cleansing, Predictive Analytics, No-Code Development, Statistical Programming, Performance Tuning

  • 状态:预览

    The University of Chicago

    您将获得的技能: 机器学习, 统计方法, 分类与回归树 (CART), 回归分析, 无监督学习, 监督学习, 机器学习算法, 人工神经网络, 随机森林算法, 降维, 决策树学习, Pandas(Python 软件包), 应用机器学习, 功能工程, 张力流, Scikit-learn (机器学习库), 深度学习

  • 状态:预览

    您将获得的技能: Feature Engineering, Deep Learning, Statistical Machine Learning, Artificial Neural Networks, Supervised Learning, Machine Learning Algorithms, Applied Machine Learning, Decision Tree Learning, Machine Learning, Random Forest Algorithm, Unsupervised Learning, Dimensionality Reduction, Regression Analysis

  • 您将获得的技能: Feature Engineering, Data Visualization Software, Data Cleansing, Classification And Regression Tree (CART), Random Forest Algorithm, Decision Tree Learning, Scikit Learn (Machine Learning Library), Applied Machine Learning, Predictive Modeling, Data Manipulation, Data Science, Data Transformation, Data Processing, Predictive Analytics, Machine Learning, Python Programming

  • 状态:预览

    Sungkyunkwan University

    您将获得的技能: Data Processing, Portfolio Management, Investment Management, Classification And Regression Tree (CART), Statistical Machine Learning, Investments, Machine Learning Algorithms, Applied Machine Learning, R Programming, Feature Engineering, Machine Learning, Financial Modeling, Predictive Modeling, Decision Tree Learning, Random Forest Algorithm, Asset Management

  • 您将获得的技能: Data Cleansing, R Programming, Statistical Analysis, R (Software), Data Manipulation, Classification And Regression Tree (CART), Advanced Analytics, Statistical Modeling, Random Forest Algorithm, Statistical Methods, Predictive Modeling, Exploratory Data Analysis, Feature Engineering, Machine Learning, Dimensionality Reduction

  • 状态:预览

    您将获得的技能: Unsupervised Learning, Regression Analysis, Exploratory Data Analysis, Time Series Analysis and Forecasting, Data Analysis, Statistical Analysis, Data Science, Forecasting, Data Mining, Machine Learning, Predictive Modeling, Classification And Regression Tree (CART), Supervised Learning, Data Quality, Anomaly Detection, Feature Engineering, Dimensionality Reduction, Business Intelligence, Random Forest Algorithm

  • 状态:预览

    您将获得的技能: Data Mining, Health Informatics, Big Data, Applied Machine Learning, Predictive Modeling, Machine Learning, Artificial Intelligence, Random Forest Algorithm, Clinical Practices, Responsible AI, Deep Learning, Health Care, Unsupervised Learning, Algorithms, Data Cleansing, Artificial Neural Networks

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