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glossary [2022/09/12 11:57] – adminglossary [2022/09/12 16:51] (current) – admin
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-====== Glossary of machine learning terminology ======+====== Glossary of machine learning terms ======
  
 ===== A ===== ===== A =====
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 TP…[[:glossary#true_positive_tp|true positive]], TN…[[:glossary#true_negative_tn|true negative]], FP…[[:glossary#false_positive_fp|false positive]], FN…[[:glossary#false_negative_fn|false negative]] TP…[[:glossary#true_positive_tp|true positive]], TN…[[:glossary#true_negative_tn|true negative]], FP…[[:glossary#false_positive_fp|false positive]], FN…[[:glossary#false_negative_fn|false negative]]
 +
 +==== activation function ====
 +
 +A function that defines the output of a layer in a [[:glossary#neural_network|neural network]] given an input from the previous layer (e.g. [[:glossary#rectified_linear_unit_relu|ReLU]]).
  
 ==== active learning ==== ==== active learning ====
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 ===== B ===== ===== B =====
  
-==== binary classification ====+==== batch normalisation ==== 
 + 
 +A method that makes the training of a [[:glossary#deep_neural_network|deep neural network]] faster and more stable. It consists of normalising the input and ouput of an [[:glossary#activation_function|activation function]] in a [[:glossary#hidden_layer|hidden layer]].
  
 ===== C ===== ===== C =====
 +
 +==== class ====
 +
 +One of a set of target values for a [[:glossary#label|label]].
  
 ==== classification ==== ==== classification ====
  
-The [[:glossary#prediction|prediction]] of a model is a category.+The [[:glossary#prediction|prediction]] of a model is a category, i.e. a discrete [[:glossary#class|class]].
  
 ==== clustering ==== ==== clustering ====
  
 Grouping of data, particulary during [[:glossary#unsupervised_learning|unsupervised learning]]. There exist many clustering algorithms. Grouping of data, particulary during [[:glossary#unsupervised_learning|unsupervised learning]]. There exist many clustering algorithms.
 +
 +==== convolutional layer ====
 +
 +A layer in a [[:glossary#deep_neural_network|deep neural network]] in which a convolutional filter passes over the input matrix.
  
 ==== convolutional neural network (CNN) ==== ==== convolutional neural network (CNN) ====
 +
 +A neural network in which at least one layer is a [[:glossary#convolutional_layer|convolutional layer]].
  
 ==== cross-validation ==== ==== cross-validation ====
 +
 +A method to estimate how well a model will generalise to new data. In cross-validation, the model is trained on a subset of the data and then validated on the remaining non-overlapping subsets, e.g. [[:glossary#k-fold_cross-validation|k-fold cross-validation]].
  
 ===== D ===== ===== D =====
 +
 +==== data imbalance ====
 +
 +When the [[:glossary#label|labels]] of the [[:glossary#class|classes]] have significantly different statistical distributions in the data set. It is also termed class-imbalanced data set.
  
 ==== deep learning ==== ==== deep learning ====
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 ==== feature engineering ==== ==== feature engineering ====
  
-The process of converting data into useful [[:glossary#feature|features]] for training a model. [[:glossary#feature_selection|Feature selection]] is a part of feature engineering.+The process of converting data into useful [[:glossary#feature|features]] for training a model.
  
 ==== feature selection ==== ==== feature selection ====
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 ==== feature vector ==== ==== feature vector ====
 +
 +A list of [[:glossary#feature|features]] passed into a model.
  
 ===== G ===== ===== G =====
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 ==== hidden layer ==== ==== hidden layer ====
 +
 +Artificial layer in a [[:glossary#neural_network|neural network]] between input and output layer. Typically, hidden layers contain [[:glossary#activation_function|activation functions]].
  
 ==== hierarchical agglomerative clustering ==== ==== hierarchical agglomerative clustering ====
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 ==== hyperparameters ==== ==== hyperparameters ====
 +
 +Higher-level properties of a model, such as the learning rate (how fast it can learn) or the number of [[:glossary#hidden_layer|hidden layers]].
  
 ===== I ===== ===== I =====
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 ===== K ===== ===== K =====
  
-==== k-fold validation ====+==== k-fold cross-validation ==== 
 + 
 +The training set is split into k smaller subsets. The model is trained on one of the k folds as training set and validated on the remaining (k-1) folds. This is done for all k folds. The performance measure calculated by the k-fold cross-validation is the average of the results of all k folds.
  
 ===== L ===== ===== L =====
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 ==== recall ==== ==== recall ====
 +
 +==== rectified linear unit (ReLU) ====
 +
 +An [[:glossary#activation_function|activation function]] defined as follows:
 +
 +  * If the input is negative or zero, the ouput is zero.
 +  * if the input is positive, the output is equal to the input.
  
 ===== S ===== ===== S =====
  
 ==== supervised learning ==== ==== supervised learning ====
 +
 +A [[:glossary#label|labeled data set]] is used to train a [[:glossary#model|model]].
  
 ===== T ===== ===== T =====
glossary.1662976648.txt.gz · Last modified: 2022/09/12 11:57 by admin