Machine learning uses algorithms to turn a data set into a model that can identify patterns or make predictions from new data. Which algorithm works best depends on the problem.

Some common techniques, listed from less complex to more complex, are: linear regression, linear lasso regression, linear ridge regression, k-nearest neighbors regression, (plain) kernel regression, ...

Linear ridge regression (LRR) is a relatively simple variation of standard linear regression. However, LRR and standard linear regression are usually considered distinct techniques, in part because ...

Linear regression analyzes two separate variables in order to define a single relationship. In chart analysis, this refers to the variables of price and time.

In the example below, I use an e-commerce data set to build a regression model. I also explain how to determine if the model reveals anything statistically significant, as well as how outliers may ...

Blazers guard Anfernee Simons has regressed this season and it's a real problem for Portland.

One useful tool to help us make sense of these kinds of problems is regression. Regression is a statistical method that allows us to look at the relationship between two variables, while holding other ...

Computations that involve matrix algorithms are happening everywhere in the world at every moment in time, whether these be embedded in the training of neural networks in data science, in computer ...