**How Concerned Should You be About Predictor Collinearity? It Depends…** - Aug 15, 2019.

Predictor collinearity (also known as multicollinearity) can be problematic for your regression models. Check out these rules of thumb about when, and when not, to be concerned.

Tags: Collinearity, Correlation, Linear Regression, Prediction

**Coding Random Forests in 100 lines of code*** - Aug 7, 2019.

There are dozens of machine learning algorithms out there. It is impossible to learn all their mechanics; however, many algorithms sprout from the most established algorithms, e.g. ordinary least squares, gradient boosting, support vector machines, tree-based algorithms and neural networks.

Tags: Algorithms, Collinearity, Machine Learning, R, Random Forests

**Feature selection by random search in Python** - Aug 6, 2019.

Feature selection is one of the most important tasks in machine learning. Learn how to use a simple random search in Python to get good results in less time.

Tags: Collinearity, Cross-validation, Feature Selection, Python, Random

**How do you check the quality of your regression model in Python?** - Jul 2, 2019.

Linear regression is rooted strongly in the field of statistical learning and therefore the model must be checked for the ‘goodness of fit’. This article shows you the essential steps of this task in a Python ecosystem.

Tags: Collinearity, Data Science, Python, Regression, Statistics

**Common mistakes when carrying out machine learning and data science** - Dec 6, 2018.

We examine typical mistakes in Data Science process, including wrong data visualization, incorrect processing of missing values, wrong transformation of categorical variables, and more. Learn what to avoid!

Tags: Collinearity, Data Preparation, Data Science, Data Visualization, Machine Learning, Missing Values, Mistakes