Stacked generalization, or stacking, may be a less popular machine learning ensemble given that it describes a framework more than a specific model. Perhaps the reason it has been less popular in mainstream machine learning is that it can be tricky to train a stacking model correctly, without suffering data leakage. This has meant that […]
Recommender systems may be the most common type of predictive model that the average person may encounter. They provide the basis for recommendations on services such as Amazon, Spotify, and Youtube. Recommender systems are a huge daunting topic if you’re just getting started. There is a myriad of data preparation techniques, algorithms, and model evaluation […]
Optimization is a field of mathematics concerned with finding a good or best solution among many candidates. It is an important foundational topic required in machine learning as most machine learning algorithms are fit on historical data using an optimization algorithm. Additionally, broader problems, such as model selection and hyperparameter tuning, can also be framed […]
Genetic Programming (GP) is an algorithm for evolving programs to solve specific well-defined problems. It is a type of automatic programming intended for challenging problems where the task is well defined and solutions can be checked easily at a low cost, although the search space of possible solutions is vast, and there is little intuition […]
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 […]
Computational learning theory, or statistical learning theory, refers to mathematical frameworks for quantifying learning tasks and algorithms. These are sub-fields of machine learning that a machine learning practitioner does not need to know in great depth in order to achieve good results on a wide range of problems. Nevertheless, it is a sub-field where having […]
Data preparation is the transformation of raw data into a form that is more appropriate for modeling. It is a challenging topic to discuss as the data differs in form, type, and structure from project to project. Nevertheless, there are common data preparation tasks across projects. It is a huge field of study and goes […]
Classification accuracy is the total number of correct predictions divided by the total number of predictions made for a dataset. As a performance measure, accuracy is inappropriate for imbalanced classification problems. The main reason is that the overwhelming number of examples from the majority class (or classes) will overwhelm the number of examples in the […]
Classification is a predictive modeling problem that involves predicting a class label for a given example. It is generally assumed that the distribution of examples in the training dataset is even across all of the classes. In practice, this is rarely the case. Those classification predictive models where the distribution of examples across class labels […]
Predictive modeling with deep learning is a skill that modern developers need to know. TensorFlow is the premier open-source deep learning framework developed and maintained by Google. Although using TensorFlow directly can be challenging, the modern tf.keras API brings Keras’s simplicity and ease of use to the TensorFlow project. Using tf.keras allows you to design, […]
