Week 3, Part 0a, Decision Tree Construction in Python (using scikit-learn) By Jun Li December 16, 2020 Data Science 86 data analytics, data science, data scientist, data scientists, data visualization, deep learning python, jupyter notebook, machine learning, matplotlib, neural networks python, nlp python, numpy python, python data, python pandas, python seaborn, python sklearn, tensor flow python, tips tricks python 7 Comments [ad_1] Source [ad_2] Previous article Next article Comment List Jun Li December 16, 2020 Thank you, very good explanation. If anyone reads this comment from INDONESIA or wants to see tutorials on Decision Tree topic with Bahasa Indonesia, i also have another good video.https://youtu.be/sgnOaSpB5co (Ngoding decision tree di python3 + optimasi) Reply Jun Li December 16, 2020 Okay, so load_iris is not recognized, and print type (data) yields bad syntax error Reply Jun Li December 16, 2020 Weka was used to construct the j48 algorithm for decision tree or PART. I want to utilize python to do so and finally, I can develop model and visualization. I am using demographic and health surveys. Reply Jun Li December 16, 2020 Sir, how can we visualize decision tree in python? Reply Jun Li December 16, 2020 Hi teacher , At the end of the video, it shows that the classification accuracy is 1, Is that means overfitting. Waiting for your answer, because i trained my own dataset , it also result in 1, Thank you Reply Jun Li December 16, 2020 Hi Teacher,How to get the dataset to train please?I'm a newbie and i want to learn python and data science !THANKS! Reply Jun Li December 16, 2020 can you help me out with a machine learning problem email firstname.lastname@example.org Reply Write a comment Cancel reply Save my name, email, and website in this browser for the next time I comment.