Exploring the Decision Tree Algorithm in Python! - A Step-by-Step Machine Learning Tutorial смотреть онлайн

Learn how to implement the Decision Tree Classifier machine learning algorithm in Python - all from SCRATCH! From in-depth explanations to detailed code descriptions, this video is the perfect tutorial for anyone looking to experiment and grow more familiar with the Decision Tree.

- Feel free to access the Kaggle Dataset used throughout this video here: https://www.kaggle.com/datasets/meet3010/cars-brand-prediction.
- The GitHub Script containing the Jupyter Notebook we worked on can be found here: https://github.com/Rohak72/Machine-Learning-Algorithms/blob/main/Algorithms/Decision%20Tree.ipynb.
- Additionally, if you want to learn more about the 'plot_tree' function that was used to visualize the decision tree, visit the following page: https://scikit-learn.org/stable/modules/generated/sklearn.tree.plot_tree.html.

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?Timestamps:
0:00 Intro
0:38 Data Pre-Processing and Cleaning
3:25 Building our Decision Tree Classifier!
4:45 Visualizing the Tree Hierarchy with the 'plot_tree' function!
7:13 Gaging Model Accuracy
8:17 Concluding Thoughts

All B-Roll footage shown was sourced from pixabay.com/videos.
Free-to-use background music was downloaded from chosic.com/free-music/all.

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