Machine learning terms

See also: Machine learning and 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