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.

Over-fitting vs Under-fitting in Machine Learning - datajango

Overfitting and Underfitting Principles, by Dimid

Underfitting and Overfitting. Explaining and solving bad models

3.1 Kaggle Competion Do A Data Science Project in 10 Days

Overfitting vs. Underfitting: What Is the Difference?

Overfitting, underfitting, and the bias-variance tradeoff

10.2 General Cross Validation Methods

2.2 Downlaod and Install R and RStudio

4.4. Model Selection, Underfitting, and Overfitting — Dive into

3.11. Model Selection, Underfitting and Overfitting — Dive into

Chapter 7 Regression I: K-nearest neighbors

4.4. Model Selection, Underfitting, and Overfitting — Dive into

2.3 Bootsup your RStudio Do A Data Science Project in 10 Days

Overfitting and Underfitting in Machine Learning + [Example]