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Table of Contents
Glossary of machine learning terminology
A
accuracy
Percentage of correct predictions by a classification model.
It is defined as $$ \frac{TP + TN}{TP + FP + FN + TN}\,.$$
TP…true positive, TN…true negative, FP…false positive, FN…false negative
active learning
An ML approach in which the algorithm chooses the data to learn from. An active learning approach is particularly useful when there is a lot of unlabeled data and manual labeling is very expensive. Often, the number of examples to learn from is lower than when blindly seeking a diverse range of labeled examples in normal supervised learning.
B
binary classification
C
classification
The prediction of a model is a category.
clustering
Grouping of data, particulary during unsupervised learning. There exist many clustering algorithms.
convolutional neural network (CNN)
cross-validation
D
deep learning
deep neural network
A type of neural network containing multiple hidden layers.
E
early stopping
epoch
Describes the number of times the algorithm sees the whole data set.
F
F1
false negative (FN)
false positive (FN)
false positive rate (FPR)
feature
An input variable for making predictions.
feature engineering
The process of converting data into useful features for training a model. Feature selection is a part of feature engineering.
feature selection
The process of selecting relevant features from a data set.
feature vector
G
H
hidden layer
hierarchical agglomerative clustering
A clustering approach that creates a tree of clusters, specifially well-suited for hierarchically organised data. In a first step, the algorithm assigns a cluster to each example. In a second step, it merges the closest clusters to create a hierarchical tree.
hyperparameters
I
J
K
k-fold validation
L
label
long short-term memory (LSTM)
loss
M
model
multi-class classification
N
neural network
P
precision
prediction
Output of a model.