Source of Arthur Samuel's definition of machine learning
14 Many people seem to agree that Arthur Samuel wrote or said in 1959 that machine learning is the " Field of study that gives computers the ability to learn without being explicitly …
machine learning - What is the difference between AI, ML, NN and …
Nov 18, 2020 · Machine Learning (ML) Neural Network (NN) Deep Learning (DL) Data Science My current understanding is that each of these encapsulates some set of algorithms. I feel like, …
machine learning - What is Ground Truth - Data Science Stack …
In machine learning, the term "ground truth" refers to the accuracy of the training set's classification for supervised learning techniques. This is used in statistical models to prove or …
machine learning - What's The Difference Between The Terms …
The difference is likely in the community that uses the terms. For computer vision/ml, the term "feature" is commonly used.
machine learning - How to train ML model with multiple variables ...
Oct 2, 2018 · I am trying to learn Machine Learning concepts these days. I understand in a traditional ML data, we will have features and labels. I have following toy data in my mind …
Best practices to store Python machine learning models
What are the best practices to save, store, and share machine learning models? In Python, we generally store the binary representation of the model, using pickle or joblib.
machine learning - Do model training pipeline should run on dev ...
Feb 21, 2023 · Do we train the model in three different environments? (IMO, No, but looking for justifications and understanding industry best practices) How do the dev, staging, and …
machine learning - How many features is too many when using …
Jul 9, 2023 · Probably use ML based techniques (like Forest or Boosting based importances etc). This is just my opinion and preferance as this has worked for me pretty well, but many people …
machine learning - Keywords for: 'train ML model with data from …
Sep 15, 2025 · I need to find articles in which the authors trained machine learning model(s) with data from one geographical place, and, afterwards, applied the trained model on data about …
Training set and test set size - Data Science Stack Exchange
Jan 19, 2022 · In the machine learning world, data scientists are often told to train a supervised model on a large training dataset and test it on a smaller amount of data. The reason why …