How to Calculate McNemar’s Test to Compare Two Machine Learning Classifiers

Last Updated on August 8, 2019 The choice of a statistical hypothesis test is a challenging open problem for interpreting machine learning results. In his widely cited 1998 paper, Thomas Dietterich recommended the McNemar’s test in those cases where it is expensive or impractical to train multiple copies of classifier models. This describes the current situation with deep learning models that are both very large and are trained and evaluated on large datasets, often requiring days or weeks to train […]

September 29, 2020 Machine Learning

How to Configure the Number of Layers and Nodes in a Neural Network

Last Updated on August 6, 2019 Artificial neural networks have two main hyperparameters that control the architecture or topology of the network: the number of layers and the number of nodes in each hidden layer. You must specify values for these parameters when configuring your network. The most reliable way to configure these hyperparameters for your specific predictive modeling problem is via systematic experimentation with a robust test harness. This can be a tough pill to swallow for beginners to […]

September 29, 2020 Machine Learning

How to Code the Student’s t-Test from Scratch in Python

Last Updated on August 8, 2019 Perhaps one of the most widely used statistical hypothesis tests is the Student’s t test. Because you may use this test yourself someday, it is important to have a deep understanding of how the test works. As a developer, this understanding is best achieved by implementing the hypothesis test yourself from scratch. In this tutorial, you will discover how to implement the Student’s t-test statistical hypothesis test from scratch in Python. After completing this […]

September 29, 2020 Machine Learning

Why Initialize a Neural Network with Random Weights?

Last Updated on March 26, 2020 The weights of artificial neural networks must be initialized to small random numbers. This is because this is an expectation of the stochastic optimization algorithm used to train the model, called stochastic gradient descent. To understand this approach to problem solving, you must first understand the role of nondeterministic and randomized algorithms as well as the need for stochastic optimization algorithms to harness randomness in their search process. In this post, you will discover […]

September 29, 2020 Machine Learning

Statistics for Machine Learning (7-Day Mini-Course)

Last Updated on August 8, 2019 Statistics for Machine Learning Crash Course. Get on top of the statistics used in machine learning in 7 Days. Statistics is a field of mathematics that is universally agreed to be a prerequisite for a deeper understanding of machine learning. Although statistics is a large field with many esoteric theories and findings, the nuts and bolts tools and notations taken from the field are required for machine learning practitioners. With a solid foundation of […]

September 29, 2020 Machine Learning

11 Classical Time Series Forecasting Methods in Python (Cheat Sheet)

Last Updated on August 20, 2020 Machine learning methods can be used for classification and forecasting on time series problems. Before exploring machine learning methods for time series, it is a good idea to ensure you have exhausted classical linear time series forecasting methods. Classical time series forecasting methods may be focused on linear relationships, nevertheless, they are sophisticated and perform well on a wide range of problems, assuming that your data is suitably prepared and the method is well […]

September 29, 2020 Machine Learning

Taxonomy of Time Series Forecasting Problems

Last Updated on August 5, 2019 When you are presented with a new time series forecasting problem, there are many things to consider. The choice that you make directly impacts each step of the project from the design of a test harness to evaluate forecast models to the fundamental difficulty of the forecast problem that you are working on. It is possible to very quickly narrow down the options by working through a series of questions about your time series […]

September 29, 2020 Machine Learning

How to Develop a Skillful Machine Learning Time Series Forecasting Model

Last Updated on August 5, 2019 You are handed data and told to develop a forecast model. What do you do? This is a common situation; far more common than most people think. Perhaps you are sent a CSV file. Perhaps you are given access to a database. Perhaps you are starting a competition. The problem can be reasonably well defined: You have or can access historical time series data. You know or can find out what needs to be […]

September 29, 2020 Machine Learning

How to Reduce Variance in a Final Machine Learning Model

Last Updated on June 26, 2020 A final machine learning model is one trained on all available data and is then used to make predictions on new data. A problem with most final models is that they suffer variance in their predictions. This means that each time you fit a model, you get a slightly different set of parameters that in turn will make slightly different predictions. Sometimes more and sometimes less skillful than what you expected. This can be […]

September 29, 2020 Machine Learning

17 Statistical Hypothesis Tests in Python (Cheat Sheet)

Last Updated on November 28, 2019 Quick-reference guide to the 17 statistical hypothesis tests that you need inapplied machine learning, with sample code in Python. Although there are hundreds of statistical hypothesis tests that you could use, there is only a small subset that you may need to use in a machine learning project. In this post, you will discover a cheat sheet for the most popular statistical hypothesis tests for a machine learning project with examples using the Python […]

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