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| <noinclude>{{see also|Terms|Machine learning}} | | <noinclude>{{see also|Terms|Machine learning}} |
| </noinclude><includeonly>=</includeonly>==Fundamentals==<includeonly>=</includeonly>
| | =</includeonly>==Fundamentals==<includeonly>=</includeonly> |
| *[[accuracy]]
| | {{see also|Machine learning terms/Fundamentals}} |
| *[[activation function]]
| | {{:Machine learning terms/Fundamentals}} |
| *[[artificial intelligence]]
| | |
| *[[AUC (Area Under the Curve)]]
| | |
| *[[backpropagation]]
| | =</includeonly>==All==<includeonly>=</includeonly> |
| *[[batch]]
| | {{see also|Machine learning terms/All}} |
| *[[batch size]]
| | {{:Machine learning terms/All}} |
| *[[bias]]
| |
| *[[bias (ethics/fairness)]]
| |
| *[[binary classification]]
| |
| *[[bucketing]]
| |
| *[[categorical data]]
| |
| *[[class]]
| |
| *[[classification model]]
| |
| *[[classification threshold]]
| |
| *[[class-imbalanced dataset]]
| |
| *[[clipping]]
| |
| *[[confusion matrix]]
| |
| *[[continuous feature]]
| |
| *[[convergence]]
| |
| *[[DataFrame]]
| |
| *[[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 inference]]
| |
| *[[online learning]]
| |
| *[[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]]
| |
| *[[stability]]
| |
| *[[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]]
| |
