Ways to think about machine learning (2018)

Dec 5, 2023 ·

Machine learning is an area of artificial intelligence that involves the use of algorithms to analyze data and make predictions and decisions. It has a wide range of applications, including natural language processing, computer vision, predictive analytics, and robotics. In this article, Ben Evans discusses eight ways of thinking about machine learning.

Firstly, he explains the concept of 'black box' learning, which refers to models where the underlying mathematical relationships are not known. This is contrasted with 'white box' learning, where the model's structure is known. He also notes that it is important to consider the difference between supervised and unsupervised learning. Supervised algorithms require labeled data in order to make accurate predictions, whereas unsupervised algorithms can work with unlabeled data.

Next, Evans outlines the importance of feature engineering, which involves selecting and transforming data so that it can be used as input for a machine learning model. He also emphasizes the need to think about training and test datasets: training sets are used to train the model, and test sets are used to evaluate its performance. Additionally, he covers the role of evaluation metrics, such as accuracy and recall, in measuring model performance.

Finally, the article highlights the use of ensembles, which combine multiple models to improve their performance. He also touches on the challenge of dealing with imbalanced datasets, and suggests using techniques such as oversampling and undersampling to reduce the impact of these issues.

Overall, this article provides a useful overview of some of the key concepts in machine learning and how they can be applied. By understanding the different ways to approach machine learning problems, developers can develop more effective and efficient solutions.