Author: Eryk Lewinson Translator: Wang Anxu Proofreader: zrx This article is approximately 4400 words long and is recommended for a 5-minute read. This article explores three methods of creating meaningful features using time-related information. Tags: Time Frame, Machine Learning, Python, Technical Demonstration Imagine you are starting a new data science project. The goal is to … Read more
In any supervised machine learning project, the purpose of training a model is to learn the optimal values of weights and biases from labeled examples. If we use the same labeled examples to test our model, it would be a methodological error, as a model that simply repeats the labels of the samples it has … Read more
Source: Not Just Data Analysis This article is about 5800 words long, and it is recommended to read for over 10 minutes. Without statistics, machine learning cannot exist, but due to the contemporary information explosion and the vast amount of data humans can access, machine learning is extremely useful. The distinction between statistics and machine … Read more
Machine learning is a topic everyone is discussing, but aside from teachers who have a deep understanding, very few can explain what it is clearly. If you read articles about machine learning online, you are likely to encounter two scenarios: dense academic texts filled with various theorems (I can barely handle half a theorem) or … Read more
1. Overview of Machine Learning 1) What is Machine Learning Artificial Intelligence is a new technical science that studies and develops theories, methods, technologies, and application systems for simulating, extending, and enhancing human intelligence. It is a broad and vague concept, with the ultimate goal of artificial intelligence being to enable computers to mimic human … Read more
Source: Machine Learning Community, Data Science THU During the model building phase of any supervised machine learning project, the goal of training the model is to learn the best values for weights and biases from labeled examples. If we use the same labeled examples to test our model, it will be a methodological error because … Read more
Overview of MLR3 MLR3 Framework mlr3 is an object-oriented, extensible machine learning framework focused on regression, classification, survival analysis, and other machine learning tasks. It is the successor to mlr, providing efficient model building and comparison for machine learning. Some key features of mlr3 include: Object-Oriented Design: Implements a clean object-oriented design using R6. Optimized … Read more
Source: Dolphin Intelligent Science Laboratory The idea of building a machine learning or deep learning model follows the principle of constructive feedback. You build a model, get feedback from the metrics, improve it, and keep going until you achieve the desired classification accuracy. Evaluation metrics explain the performance of the model. An important aspect of … Read more
All algorithms in machine learning rely on minimizing or maximizing a function, which we call the “objective function.” The function that is minimized is called the “loss function,” which measures the model’s ability to predict the expected outcome. The most commonly used method for minimizing the loss function is the “gradient descent method.” You can … Read more
Madio.net Mathematics China /// Editor: Yu Dizongxuan This article is reprinted from the Mathematics Algorithm Club The distinction between statistics and machine learning has always been vague.Both in industry and academia, it has been widely believed that machine learning is just a shiny facade over statistics.Moreover, artificial intelligence, supported by machine learning, is also referred … Read more