https://www.analyticsvidhya.com/blog/2016/04/tree-based-algorithms-complete-tutorial-scratch-in-python/?utm_source=blog&utm_medium=decision-tree-vs-random-forest-algorithm
https://courses.analyticsvidhya.com/courses/getting-started-with-decision-trees?utm_source=blog&utm_medium=decision-tree-vs-random-forest-algorithm 注:本文的想法是比较决策树和随机森林。因此,我不会详细解释基本概念,但是我将提供相关链接以便于你可以进一步探究。
https://www.analyticsvidhya.com/blog/2018/12/building-a-random-forest-from-scratch-understanding-real-world-data-products-ml-for-programmers-part-3/?utm_source=blog&utm_medium=decision-tree-vs-random-forest-algorithm
https://www.analyticsvidhya.com/blog/2020/03/beginners-guide-random-forest-hyperparameter-tuning/?utm_source=blog&utm_medium=decision-tree-vs-random-forest-algorithm
https://www.analyticsvidhya.com/blog/2018/06/comprehensive-guide-for-ensemble-models/?utm_source=blog&utm_medium=decision-tree-vs-random-forest-algorithm
注:你可以去DataHack(https://datahack.analyticsvidhya.com/contest/all/?utm_source=blog&utm_medium=decision-tree-vs-random-forest-algorithm )平台并在不同在线机器学习竞赛中与他人竞争,并且有机会获得令人兴奋的奖品。 https://www.analyticsvidhya.com/blog/2016/07/practical-guide-data-preprocessing-python-scikit-learn/?utm_source=blog&utm_medium=decision-tree-vs-random-forest-algorithm
第三步:创造训练集和测试集 现在,让我们以80:20的比例进行训练集和测试集的划分:
让我们一眼所划分的训练集和测试集:
https://www.analyticsvidhya.com/blog/2019/08/11-important-model-evaluation-error-metrics/?utm_source=blog&utm_medium=decision-tree-vs-random-forest-algorithm
为什么我们的随机森林模型比决策树表现更好?
https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.BaggingClassifier.html#sklearn.ensemble.BaggingClassifier https://www.analyticsvidhya.com/blog/2019/08/decoding-black-box-step-by-step-guide-interpretable-machine-learning-models-python/?utm_source=blog&utm_medium=decision-tree-vs-random-forest-algorithm 原文标题: Decision Tree vs. Random Forest – Which Algorithm Should you Use? 原文链接: https://www.analyticsvidhya.com/blog/2020/05/decision-tree-vs-random-forest-algorithm/ |