A validation dataset is a sample of data held back from training your model that is used to give an estimate of model skill while tuning model’s hyperparameters. The validation dataset is different from the test dataset that is also held back from the training of the model, but is instead used to give an unbiased […]
Search results for "Artificial Intelligence"
How to Calculate Bootstrap Confidence Intervals For Machine Learning Results in Python
It is important to both present the expected skill of a machine learning model a well as confidence intervals for that model skill. Confidence intervals provide a range of model skills and a likelihood that the model skill will fall between the ranges when making predictions on new data. For example, a 95% likelihood of […]
How to Evaluate the Skill of Deep Learning Models
I often see practitioners expressing confusion about how to evaluate a deep learning model. This is often obvious from questions like: What random seed should I use? Do I need a random seed? Why don’t I get the same results on subsequent runs? In this post, you will discover the procedure that you can use […]
Estimate the Number of Experiment Repeats for Stochastic Machine Learning Algorithms
A problem with many stochastic machine learning algorithms is that different runs of the same algorithm on the same data return different results. This means that when performing experiments to configure a stochastic algorithm or compare algorithms, you must collect multiple results and use the average performance to summarize the skill of the model. This […]
5 Top Machine Learning Podcasts
Machine learning podcasts are now a thing. There are now enough of us interested in this obscure geeky topic that there are podcasts dedicated to chatting about the ins and outs of predictive modeling. There has never been a better time to get started and working in this amazing field. In this post, I want […]
Python is the Growing Platform for Applied Machine Learning
You should pick the right tool for the job. The specific predictive modeling problem that you are working on should dictate the specific programming language, libraries and even machine learning algorithms to use. But, what if you are just getting started and looking for a platform to learn and practice machine learning? In this post, […]
How Beginners Get It Wrong In Machine Learning
The 5 Most Common Mistakes That Beginners Make And How To Avoid Them. I help beginners get started in machine learning. But I see the same mistakes in both mindset and action again and again. In this post, you will discover the 5 most common ways that I see beginners slip-up when getting started in machine […]
Embrace Randomness in Machine Learning
Why Do You Get Different Results On Different Runs Of An Algorithm With The Same Data? Applied machine learning is a tapestry of breakthroughs and mindset shifts. Understanding the role of randomness in machine learning algorithms is one of those breakthroughs. Once you get it, you will see things differently. In a whole new light. Things like […]
A Gentle Introduction to the Gradient Boosting Algorithm for Machine Learning
Gradient boosting is one of the most powerful techniques for building predictive models. In this post you will discover the gradient boosting machine learning algorithm and get a gentle introduction into where it came from and how it works. After reading this post, you will know: The origin of boosting from learning theory and AdaBoost. How […]
8 Inspirational Applications of Deep Learning
It is hyperbole to say deep learning is achieving state-of-the-art results across a range of difficult problem domains. A fact, but also hyperbole. There is a lot of excitement around artificial intelligence, machine learning and deep learning at the moment. It is also an amazing opportunity to get on on the ground floor of some really powerful tech. […]