中By the end of this course, learners will build, interpret, and evaluate decision tree models in R for both classification and regression tasks. They will gain hands-on skills in data preprocessing, feature engineering, and model training, while applying predictive techniques to real-world datasets including advertisements, diabetes outcomes, Caeseats sales, and bank loan defaults.
Master Decision Trees in R: Build, Predict & Evaluate
您将学到什么
Preprocess data, engineer features, and train decision tree models in R.
Visualize results and evaluate performance using confusion matrix and metrics.
Apply classification and regression trees to real-world business and financial cases.
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September 2025
作业
13 项作业
授课语言：英语（English）
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该课程共有4个模块
This module introduces learners to the fundamentals of decision tree modeling using R. It covers the basics of tree structure, data preparation, and the creation of classification models. By the end of this module, learners will understand how to preprocess data, construct decision trees, and evaluate model performance effectively.
涵盖的内容
8个视频4个作业1个插件
This module introduces learners to the fundamentals of Decision Tree modeling and its application in Bank Loan Default Prediction. Participants will explore the basics of analytics, understand the problem statement, and prepare their tools and datasets in R to begin predictive modeling with confidence.
涵盖的内容
5个视频3个作业
This module explores advanced applications of decision trees in R, focusing on real-world datasets, regression trees, and visualization. Learners will practice prediction tasks, implement splitting strategies, and compare R packages for decision tree modeling.
涵盖的内容
6个视频3个作业
This module focuses on applying Decision Tree modeling in R by preparing datasets, training models, and evaluating predictive performance. Learners will gain hands-on experience in coding, interpreting results using a confusion matrix, and understanding how decision trees support financial risk prediction.
涵盖的内容
5个视频3个作业
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