In this article, we’ll explain the concept of polynomial regression and show how it can lead to overfitting. We’ll also discuss some techniques you can use to avoid overfitting.
In this article, we’ll explain the concept of polynomial regression and show how it can lead to overfitting. We’ll also discuss some techniques you can use to avoid overfitting. These include using k-fold cross-validation or a hold-out set but, most importantly, we’ll discuss how applying domain knowledge will help you avoid overfitting. We won’t discuss any code by you can find the full project on GitHub.
Let’s dive straight into the concept by fitting some Linear Regression models to a dataset. We will be using a real estate valuation data set which contains information on 414 houses sold. To keep things simple, we’ll only be looking at two variables — house price of unit area and the age of the house. We can see the relationship between these two variables in Figure 1 below. The idea is to use the age of the house to try to predict the price.
Learning is a new fun in the field of Machine Learning and Data Science. In this article, we’ll be discussing 15 machine learning and data science projects.
Most popular Data Science and Machine Learning courses — August 2020. This list was last updated in August 2020 — and will be updated regularly so as to keep it relevant
An overview of the oldest supervised machine-learning algorithm, its type & shortcomings.
“How’d you get started with machine learning and data science?”: I trained my first model in 2017 on my friend's lounge room floor.
Why should you learn R programming when you're aiming to learn data science? Here are six reasons why R is the right language for you.