Machine learning terms/Fundamentals: Difference between revisions
(Created page with "*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 *dens...") |
No edit summary |
||
| (One intermediate revision by the same user not shown) | |||
| Line 1: | Line 1: | ||
*[[accuracy]] | <noinclude>{{see also|Machine learning terms}}</noinclude>*[[accuracy]] | ||
*[[activation function]] | *[[activation function]] | ||
*[[artificial intelligence]] | *[[artificial intelligence]] | ||
Latest revision as of 13:21, 26 February 2023
- See 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