10.1 Model's Underfitting and Overfitting
This is a data science project practice book. It was initially written for my Big Data course to help students to run a quick data analytical project and to understand 1. the data analytical process, the typical tasks and the methods, techniques and the algorithms need to accomplish these tasks. During convid19, the unicersity has adopted on-line teaching. So the students can not access to the university labs and HPC facilities. Gaining an experience of doing a data science project becomes individual students self-learning in isolation. This book aimed to help them to read through it and follow instructions to complete the sample propject by themslef. However, it is required by many other students who want to know about data analytics, machine learning and particularly practical issues, to gain experience and confidence of doing data analysis. So it is aimed for beginners and have no much knowledge of data Science. the format for this book is bookdown::gitbook.
Overfitting and underfitting in machine learning
8.1 Intro to overfitting and underfitting (L08: Model Evaluation
Identify the Problems of Overfitting and Underfitting - Improve
8.4 The Decision Tree with More Predictors
Underfitting vs. Overfitting — scikit-learn 1.4.1 documentation
R studio viewer - sherygb
Model Validation: Problem Areas and Solutions - Overfitting and
2.2 Downlaod and Install R and RStudio
4.4. Model Selection, Underfitting, and Overfitting — Dive into
4.4. Model Selection, Underfitting, and Overfitting — Dive into
4.4. Model Selection, Underfitting, and Overfitting — Dive into
12.3 Model Interpretation Do A Data Science Project in 10 Days
4.3 General Data Attributes Assessment
Overfitting and Underfitting Principles, by Dimid
