Last Updated on December 11, 2019 Many machine learning algorithms make assumptions about your data. It is often a very good idea to prepare your data in such way to best expose the structure of the problem to the machine learning algorithms that you intend to use. In this post you will discover how to prepare your data for machine learning in Python using scikit-learn. Kick-start your project with my new book Machine Learning Mastery With Python, including step-by-step tutorials […]

Last Updated on August 28, 2020 The data features that you use to train your machine learning models have a huge influence on the performance you can achieve. Irrelevant or partially relevant features can negatively impact model performance. In this post you will discover automatic feature selection techniques that you can use to prepare your machine learning data in python with scikit-learn. Kick-start your project with my new book Machine Learning Mastery With Python, including step-by-step tutorials and the Python […]

Last Updated on August 28, 2020 You need to know how well your algorithms perform on unseen data. The best way to evaluate the performance of an algorithm would be to make predictions for new data to which you already know the answers. The second best way is to use clever techniques from statistics called resampling methods that allow you to make accurate estimates for how well your algorithm will perform on new data. In this post you will discover […]

Last Updated on August 31, 2020 The metrics that you choose to evaluate your machine learning algorithms are very important. Choice of metrics influences how the performance of machine learning algorithms is measured and compared. They influence how you weight the importance of different characteristics in the results and your ultimate choice of which algorithm to choose. In this post, you will discover how to select and use different machine learning performance metrics in Python with scikit-learn. Kick-start your project […]

Last Updated on August 28, 2020 Spot-checking is a way of discovering which algorithms perform well on your machine learning problem. You cannot know which algorithms are best suited to your problem before hand. You must trial a number of methods and focus attention on those that prove themselves the most promising. In this post you will discover 6 machine learning algorithms that you can use when spot checking your classification problem in Python with scikit-learn. Kick-start your project with […]

Last Updated on August 28, 2020 Spot-checking is a way of discovering which algorithms perform well on your machine learning problem. You cannot know which algorithms are best suited to your problem before hand. You must trial a number of methods and focus attention on those that prove themselves the most promising. In this post you will discover 6 machine learning algorithms that you can use when spot checking your regression problem in Python with scikit-learn. Kick-start your project with […]

Last Updated on August 28, 2020 It is important to compare the performance of multiple different machine learning algorithms consistently. In this post you will discover how you can create a test harness to compare multiple different machine learning algorithms in Python with scikit-learn. You can use this test harness as a template on your own machine learning problems and add more and different algorithms to compare. Kick-start your project with my new book Machine Learning Mastery With Python, including […]