One crucial step in machine learning is the choice of model. A suitable model with suitable hyperparameter is the key to a good prediction result. When we are faced with a choice between models, how should the decision be made? This is why we have cross validation. In scikit-learn, there is a family of functions […]
Are you getting different results for your machine learning algorithm? Perhaps your results differ from a tutorial and you want to understand why. Perhaps your model is making different predictions each time it is trained, even when it is trained on the same data set each time. This is to be expected and might even […]
Given easy-to-use machine learning libraries like scikit-learn and Keras, it is straightforward to fit many different machine learning models on a given predictive modeling dataset. The challenge of applied machine learning, therefore, becomes how to choose among a range of different models that you can use for your problem. Naively, you might believe that model […]
After you develop a machine learning model for your predictive modeling problem, how do you know if the performance of the model is any good? This is a common question I am asked by beginners. As a beginner, you often seek an answer to this question, e.g. you want someone to tell you whether an […]
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 […]
So, you’re working on a machine learning problem. I want to really nail down where you’re at right now. Let me make some guesses… 1) You Have a Problem So you have a problem that you need to solve. Maybe it’s your problem, an idea you have, a question, or something you want to address. […]
How to Handle the Intractability of Applied Machine Learning. Applied machine learning is challenging. You must make many decisions where there is no known “right answer” for your specific problem, such as: What framing of the problem to use? What input and output data to use? What learning algorithm to use? What algorithm configuration to […]
Machine learning experiments can take a long time. Hours, days, and even weeks in some cases. This gives you a lot of time to think and plan for additional experiments to perform. In addition, the average applied machine learning project may require tens to hundreds of discrete experiments in order to find a data preparation […]
It can be confusing when you get started in applied machine learning. There are so many terms to use and many of the terms may not be used consistently. This is especially true if you have come from another field of study that may use some of the same terms as machine learning, but they […]
The amount of data you need depends both on the complexity of your problem and on the complexity of your chosen algorithm. This is a fact, but does not help you if you are at the pointy end of a machine learning project. A common question I get asked is: How much data do I […]
