I'd go as far as to say that "training data" is mathematical induction applied to datasets. The term has become largely associated with ML though, so to answer OP's question, one can assume that if training data is involved, ML is also involved. But the other way around - that training data is not used outside of ML - might not always be the case (although it probably is true in most cases)
Hi all, I have a very naive question: Is a method using a machine learning approach if there is training data involved? Or put differently, is "training data" exclusively used in machine learning? I found this definition for training data: Training data is a collection of labeled information that's used to build a machine learning model. Thanks!
2 answers
No, 'training data' it is a term [and strategy] that has been used for decades. There's nothing new about anything in Machine Learning, apart from it being a marketing strategy to attract funding.
Kevin
Edit: the traditional study design would be:
- pilot phase
- discovery phase
- validation phase
Training data may be used in step 2.
Machine Learning is not itself new - the term as we understand it was coined in 1959 and had its first heyday in the 70s.
Is fitting a straight line machine learning?
We use the existing data to "train" the best fit, and we can "predict" (extrapolate) from that line.
As for a better definition:
https://en.wikipedia.org/wiki/Machine_learning
Tom M. Mitchell provided a widely quoted, more formal definition of the algorithms studied in the machine learning field:
A computer program is said to learn from experience E with respect to some class of tasks T and performance measure P if its performance at tasks in T, as measured by P, improves with experience E.
thus machine learning is different from a simple formula in that in ML more data will improve the performance.
Is fitting a straight line machine learning?
Yes. The more data you have, the better the overall fit of the line will be (the smaller the standard errors).
Log in to answer this question.