What To Do If Model Test Results Are Worse than Training. The procedure when evaluating machine learning models is to fit and evaluate them on training data, then verify that the model has good skill on a held-back test dataset. Often, you will get a very promising performance when evaluating the model on the training […]
You cannot avoid mathematical notation when reading the descriptions of machine learning methods. Often, all it takes is one term or one fragment of notation in an equation to completely derail your understanding of the entire procedure. This can be extremely frustrating, especially for machine learning beginners coming from the world of development. You can […]
Caption generation is a challenging artificial intelligence problem where a textual description must be generated for a photograph. It requires both methods from computer vision to understand the content of the image and a language model from the field of natural language processing to turn the understanding of the image into words in the right […]
3 Strategies to Design Experiments and Manage Complexity on Your Predictive Modeling Problem. It is difficult to get started on a new time series forecasting project. Given years of data, it can take days or weeks to fit a deep learning model. How do you get started exactly? For some practitioners, this can lead to […]
Long Short-Term Memory or LSTM recurrent neural networks are capable of learning and remembering over long sequences of inputs. LSTMs work very well if your problem has one output for every input, like time series forecasting or text translation. But LSTMs can be challenging to use when you have very long input sequences and only […]
It can be difficult when starting out on a new predictive modeling project with neural networks. There is so much to configure, and no clear idea where to start. It is important to be systematic. You can break bad assumptions and quickly hone in on configurations that work and areas for further investigation likely to […]
You do not have to model your time series forecast problem as-is. There are many ways to reframe your forecast problem that can both simplify the prediction problem and potentially expose more or different information to be modeled. A reframing can ultimately result in better and/or more robust forecasts. In this tutorial, you will discover […]
Gradient boosting is one of the most powerful techniques for applied machine learning and as such is quickly becoming one of the most popular. But how do you configure gradient boosting on your problem? In this post you will discover how you can configure gradient boosting on your machine learning problem by looking at configurations […]
Data leakage is a big problem in machine learning when developing predictive models. Data leakage is when information from outside the training dataset is used to create the model. In this post you will discover the problem of data leakage in predictive modeling. After reading this post you will know: What is data leakage is […]
Logistic regression is another technique borrowed by machine learning from the field of statistics. It is the go-to method for binary classification problems (problems with two class values). In this post, you will discover the logistic regression algorithm for machine learning. After reading this post you will know: The many names and terms used when […]
