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I like the Machine Learning definition given by Tom Mitchell. 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.
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 programmed". For example the quote is contained in this page , that one and in Andrew Ng's ML course .
In the context of Machine Learning, I have seen the term Ground Truth used a lot. I have searched a lot and found the following definition in Wikipedia: In machine learning, the term "ground truth" refers to the accuracy of the training set's classification for supervised learning techniques.
The embedding in machine learning or NLP is actually a technique mapping from words to vectors which you can do better analysis or relating, for example, "toyota" or "honda" can be hardly related in words, but in vector space it can be set to very close according to some measure, also you can strengthen the relation ship of word by setting ...
The term "logit" is used in machine learning models that output probabilities, that is, numbers between 0 and 1. The most prominent ones are classification models, either binary classification or multi-class classification:
machine-learning; loss-function; Share. Improve this question. Follow asked Jul 23, 2018 at 19:04. d4nyll ...
A baseline is a method that uses heuristics, simple summary statistics, randomness, or machine learning to create predictions for a dataset. You can use these predictions to measure the baseline's performance (e.g., accuracy)-- this metric will then become what you compare any other machine learning algorithm against. In more detail:
The definition of VC dimension is: if there exists a set of n points that can be shattered by the classifier and there is no set of n+1 points that can be shattered by the classifier, then the VC dimension of the classifier is n. The definition does not say: if any set of n points can be shattered by the classifier...
The definition is the one on Wikipedia which you have already mentioned. The term predictor comes from statistics, here one definition: An independent variable, sometimes called an experimental or predictor variable, is a variable that is being manipulated in an experiment in order to observe the effect on a dependent variable, sometimes called ...
I am confused by the definition of the likelihood function in different contexts. In the case of linear and logistic regression, it is defined as y given x In the case naive bayes and LDA, it is