HomeWikiMachine learning terms/FundamentalsMachine learning terms/Fundamentals2 min readUpdated Mar 19, 2026EditHistorySee also: Machine learning terms accuracy activation function artificial intelligence AUC (Area Under the Curve) backpropagation batch batch size 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
