Selecting the right metric plays a huge role in evaluating the performance of a model. It can sound a bit exaggerated, but your project's chances of succeeding depend in great part on choosing a good performance measure. Making the wrong choice can result in models that are a poor fit
MAE and RMSE are some of the most common error metrics for regression problems.
Despite being used for the same task (understanding the errors in your predictions) there are important differences between the two. Choosing the right metrics for your model can make a huge difference in your ability to
Supervised learning is perhaps the most common form of machine learning task in use nowadays.
This form of learning makes use of a labeled training dataset to create a model that predicts a target of interest. If this line makes absolutely no sense to you, don't worry. After reading this
In the previous articles, we learned a definition of data science and why so many companies are investing in this field. We also learned about some of the most common scenarios where data science is the right tool for the job.
In this article (the last in the series) we
In the previous article, we learned what types of problems are a good fit for a data-science solution.
Now we will tackle the why: we will discuss the main reasons for using data science in the industry, and how companies benefit from using the power of data to improve their