Machine learning terms: Difference between revisions

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 {{see also|Machine learning|Artificial intelligence terms}}
 ==Fundamentals==
+*[[accuracy]]
+*[[activation function]]
+*[[artificial intelligence]]
+*[[AUC (Area under the ROC curve)]]
+*[[backpropagation]]
+*[[batch]]
+*[[batch size]]
+*[[bias (ethics/fairness)]]
+*[[bias (math) or bias term]]
+*[[binary classification]]
+*[[bucketing]]
+*[[categorical data]]
+*[[class]]
+*[[classification model]]
+*[[classification threshold]]
+*[[class-imbalanced dataset]]
+*[[clipping]]
+*[[confusion matrix]]
+*[[continuous feature]]
+*[[convergence]]
+*[[DataFrame]]
+*[[data set or dataset]]
+*[[deep model]]
+*[[dense feature]]
+*[[depth]]
+*[[discrete feature]]
+*[[dynamic]]
+*[[dynamic model]]
+*[[early stopping]]
+*[[embedding layer]]
+*[[epoch]]
+*[[example]]
+*[[false negative (FN)]]
+*[[false positive (FP)]]
+*[[false positive rate (FPR)]]
+*[[feature]]
+*[[feature cross]]
+*[[feature engineering]]
+*[[feature set]]
+*[[feature vector]]
+*[[feedback loop]]
+*[[generalization]]
+*[[generalization curve]]
+*[[gradient descent]]
+*[[ground truth]]
+*[[hidden layer]]
+*[[hyperparameter]]
+*[[independently and identically distributed (i.i.d)]]
+*[[inference]]
+*[[input layer]]
+*[[interpretability]]
+*[[iteration]]
+*[[L0 regularization]]
+*[[L1 loss]]
+*[[L1 regularization]]
+*[[L2 loss]]
+*[[L2 regularization]]
+*[[label]]
+*[[labeled example]]
+*[[lambda]]
+*[[layer]]
+*[[learning rate]]
+*[[linear model]]
+*[[linear]]
+*[[linear regression]]
+*[[logistic regression]]
+*[[Log Loss]]
+*[[log-odds]]
+*[[loss]]
+*[[loss curve]]
+*[[loss function]]
+*[[machine learning]]
+*[[majority class]]
+*[[mini-batch]]
+*[[minority class]]
+*[[model]]
+*[[multi-class classification]]
+*[[negative class]]
+*[[neural network]]
+*[[neuron]]
+*[[node (neural network)]]
+*[[nonlinear]]
+*[[nonstationarity]]
+*[[normalization]]
+*[[numerical data]]
+*[[offline]]
+*[[offline inference]]
+*[[one-hot encoding]]
+*[[one-vs.-all]]
+*[[online]]
+*[[online inference]]
+*[[output layer]]
+*[[overfitting]]
+*[[pandas]]
+*[[parameter]]
+*[[positive class]]
+*[[post-processing]]
+*[[prediction]]
+*[[proxy labels]]
+*[[rater]]
+*[[Rectified Linear Unit (ReLU)]]
+*[[regression model]]
+*[[regularization]]
+*[[regularization rate]]
+*[[ReLU]]
+*[[ROC (receiver operating characteristic) Curve]]
+*[[Root Mean Squared Error (RMSE)]]
+*[[sigmoid function]]
+*[[softmax]]
+*[[sparse feature]]
+*[[sparse representation]]
+*[[sparse vector]]
+*[[squared loss]]
+*[[static]]
+*[[static inference]]
+*[[stationarity]]
+*[[stochastic gradient descent (SGD)]]
+*[[supervised machine learning]]
+*[[synthetic feature]]
+*[[test loss]]
+*[[training]]
+*[[training loss]]
+*[[training-serving skew]]
+*[[training set]]
+*[[true negative (TN)]]
+*[[true positive (TP)]]
+*[[true positive rate (TPR)]]
+*[[underfitting]]
+*[[unlabeled example]]
+*[[unsupervised machine learning]]
+*[[validation]]
+*[[validation loss]]
+*[[validation set]]
+*[[weight]]
+*[[weighted sum]]
+*[[Z-score normalization]]