Classification And Regression Tree (CART)

分类与回归树(CART)是数据挖掘中用于决策过程的预测建模工具。Coursera 的 CART 目录向您传授机器学习、统计和数据分析中使用的这种强大的决策树技术。您将学习到从拆分标准、树修剪、基尼指数到 Regression 树和分类树的所有知识。您还将深入了解 CART 在医疗保健、金融和营销等各个领域的实际应用。掌握使用 CART 创建具有洞察力的预测模型的艺术,从而做出明智的决策,并提高您的 Data Analysis 和 Machine Learning 技能。
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“classification and regression tree (cart)” 的结果

  • 状态:免费试用

    University of Colorado Boulder

    您将获得的技能: Statistical Modeling, Applied Machine Learning, Data Science, Statistical Methods, Classification And Regression Tree (CART), Statistical Programming, Statistical Analysis, Supervised Learning, Regression Analysis, R Programming, Predictive Modeling, Artificial Neural Networks, Statistical Machine Learning, Dimensionality Reduction, Unsupervised Learning, Statistical Inference, Advanced Analytics, Decision Tree Learning, Machine Learning, Sampling (Statistics)

  • 状态:免费试用

    University of Colorado Boulder

    您将获得的技能: Applied Machine Learning, Supervised Learning, Data Science, Artificial Neural Networks, Dimensionality Reduction, Unsupervised Learning, Classification And Regression Tree (CART), Decision Tree Learning, Machine Learning, Predictive Modeling, Random Forest Algorithm, Statistics

  • 状态:免费试用

    Alberta Machine Intelligence Institute

    您将获得的技能: 机器学习, 分类与回归树 (CART), 回归分析, 监督学习, 数据处理, 业务解决方案, 机器学习算法, Python 程序设计, 性能分析, 性能指标, 应用机器学习, Jupyter, 功能工程, Scikit-learn (机器学习库)

  • 状态:免费试用

    University of Colorado Boulder

    您将获得的技能: 机器学习, 线性代数, 数据科学, 分类与回归树 (CART), 预测建模, 统计方法, 数据分析, 数据伦理, 数据建模, R 语言程序设计(中文版), 统计推理, 统计建模, 回归分析, 研究设计, 定量研究, 统计假设检验, 概率分布, 数学建模, 统计分析, 概率与统计

  • 状态:免费试用

    您将获得的技能: Supervised Learning, Data Modeling, Unsupervised Learning, Applied Machine Learning, Data Analysis, Regression Analysis, Classification And Regression Tree (CART), Machine Learning Algorithms, Machine Learning, Predictive Modeling, Random Forest Algorithm, Bayesian Statistics

  • 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

  • 您将获得的技能: Unsupervised Learning, Dimensionality Reduction, Supervised Learning, R Programming, Applied Machine Learning, R (Software), Tidyverse (R Package), Machine Learning, Data Science, Ggplot2, Exploratory Data Analysis, Classification And Regression Tree (CART), Feature Engineering, Random Forest Algorithm, Data Processing, Statistical Programming, Predictive Modeling, Data Manipulation

  • 状态:免费试用

    DeepLearning.AI

    您将获得的技能: 机器学习, 数据伦理, 分类与回归树 (CART), 随机森林算法, 人工神经网络, 决策树学习, 负责任的人工智能, 监督学习, 性能调整, 深度学习, 张力流

  • 状态:免费试用

    您将获得的技能: Unsupervised Learning, Time Series Analysis and Forecasting, Supervised Learning, Machine Learning, Data Processing, Feature Engineering, Artificial Intelligence, Data Cleansing, Deep Learning, Statistical Analysis, Predictive Modeling, Classification And Regression Tree (CART), Regression Analysis

  • 状态:免费试用

    您将获得的技能: Reinforcement Learning, Applied Machine Learning, Machine Learning Algorithms, Artificial Intelligence, Dimensionality Reduction, Statistical Analysis, Classification And Regression Tree (CART), Supervised Learning, Unsupervised Learning, Predictive Modeling, Random Forest Algorithm, Feature Engineering, Data Manipulation

  • 状态:免费试用

    您将获得的技能: Computer Vision, Anomaly Detection, Image Analysis, Matlab, Deep Learning, Artificial Neural Networks, Unsupervised Learning, Application Deployment, PyTorch (Machine Learning Library), Data Visualization, Artificial Intelligence and Machine Learning (AI/ML), Machine Learning Methods, Data Synthesis, Performance Tuning, Data Analysis, Classification And Regression Tree (CART), Data Validation, Medical Imaging

  • 状态:新
    状态:免费试用

    您将获得的技能: Exploratory Data Analysis, Classification And Regression Tree (CART), Predictive Modeling, Data Analysis, Regression Analysis, Supervised Learning, Machine Learning Algorithms, Statistical Machine Learning, Feature Engineering, Data Cleansing, Bayesian Network, Performance Tuning

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