If an algorithm performs better than random prediction on some class of

If an algorithm performs better than random

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If an algorithm performs better than random prediction on some class of problems then it must perform worse than random prediction on the remaining problems. Machine learning algorithms must focus on a specific problem. (cf, Tom Mitchell’s definition) 47
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Machine learning – Terminology Terminology Sample (Observation or Data): item to process ( e.g. , classify). Example: an individual, a document, a picture, a sound, a video. . . Features (Input) : set of distinct traits that can be used to describe each sample in a quantitative manner. Represented as a multi-dimensional vector usually denoted by x . Example: size, weight, citizenship, . . . Training set: Set of data used to discover potentially predictive relationships. Validation set: Set used to adjust the model hyperparameters. Evaluation/testing set: Set used to assess the performance of a model. Label (Output): The class or outcome assigned to a sample. The actual prediction is often denoted by y and the desired/targeted class by d or t . Example: man/woman, wealth, education level, . . . 48
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Machine learning – Learning approaches Learning approaches Unsupervised learning: Discovering patterns in unlabeled data. Example: cluster similar documents based on the text content. Supervised learning: Learning with a labeled training set . Example: email spam detector with training set of already labeled emails. Semisupervised learning: Learning with a small amount of labeled data and a large amount of unlabeled data . Example: web content and protein sequence classifications. Reinforcement learning: Learning based on feedback or reward. Example: learn to play chess by winning or losing. (Source: Jason Brownlee and Lucas Masuch) 49
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Machine learning – Unsupervised learning Unsupervised learning Unsupervised learning Training set: X = ( x 1 , x 2 , . . . , x N ) where x i R d . Goal: to find interesting structures in the data X . Examples: clustering, quantile estimation, outlier detection, dimensionality reduction. Statistical point of view To estimate a density p which is likely to have generated X , i.e. , such that x 1 , x 2 , . . . , x N i.i.d p (i.i.d = identically and independently distributed). 50
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Machine learning – Supervised learning Supervised learning Supervised learning A training labeled set: ( x 1 , d 1 ) , ( x 2 , d 2 ) , . . . , ( x N , d N ) . Goal: to learn a relevant mapping f st y i = f ( x i ; θ ) d i Examples: classification ( d is a categorical variable a ), regression ( d is a real variable), a . can take one of a limited, and usually fixed, number of possible values. Statistical point of view Discriminative models: to estimate the posterior distribution p ( d | x ) . Generative models: to estimate the likelihood p ( x | d ) , or the joint distribution p ( x , d ) .
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