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.

5 Most basic and must know concepts in machine learning(Set1

Chapter 9 Titiannic Prediction with Random Forest

5.8 Model tuning and avoiding overfitting

Identify the Problems of Overfitting and Underfitting - Improve

10.3 Multiple Models Comparison Do A Data Science Project in 10 Days

2.2 Downlaod and Install R and RStudio

Mastering Bias-Variance Tradeoff

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

Keeping Deep Learning Models in Check: A History-Based Approach to

12.3 Model Interpretation Do A Data Science Project in 10 Days

Underfitting vs. Overfitting — scikit-learn 1.4.1 documentation

Overfitting vs Underfitting in Machine Learning [Differences]

Two inversions illustrating the effect of underfitting and

KNN Accuracy vs K, Overfit/Underfit/Ideal? : r/learnmachinelearning

Two inversions illustrating the effect of underfitting and overfitting