Training, validation, and test data sets

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Training data set

Data set Classifier Predictive model Incremental learning Neural network

11 related topics

Overview

Machine learning Algorithms Data Mathematical model Data sets

18 related topics

Validation data set

Classification Accuracy Specificity F-measure Model selection

5 related topics

Test data set

Independent Probability distribution Holdout method Bootstrapping Nested cross-validation

5 related topics

Topics to explore

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Overview

Machine learning
Algorithms Algorithm
Data
Mathematical model
Data sets Data set
Artificial neural networks
Naive Bayes classifier
Supervised learning
Gradient descent
Stochastic gradient descent
Vector Array data structure
Variable selection Feature selection
Estimation Estimation theory
Hyperparameters Hyperparameter (machine learning)
Regularization Regularization (mathematics)
Early stopping
Over-fitting Overfitting
Cross-validation Cross-validation (statistics)

Training data set

Data set Dataset
Classifier Classifier (machine learning)
Predictive model Predictive modelling
Incremental learning
Neural network
Object detection
Starfish
Sea urchins Sea urchin
Features Feature (computer vision)
Model Computational model
False positive

Validation data set

Classification Statistical classification
Accuracy
Specificity Sensitivity and specificity
F-measure Precision and recall
Model selection

Test data set

Independent Independence (probability theory)
Probability distribution
Holdout method
Bootstrapping Bootstrapping (statistics)
Nested cross-validation

Advanced semantic analysis

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Map overview Semantic statistics

Nodes44

Edges43

Triples17

Avg. degree1.95

Density0.045455

Components1

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Important terminology Word statistics

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Important terminology

data model set training validation used test sets learning input example error overfitting performance using examples trained network multiple called

Entity relationships Subject–Predicate–Object triples

Subject	Predicate	Object	Confidence	Src
gradient descent or stochastic gradient descent	instance of	for example using optimization methods	0.80	text
over-fitting	instance of	To reduce the risk of issues	0.80	text
the examples in the validation	instance of	To reduce the risk of issues	0.80	text
test data sets should not be used to train the model.Most approaches that search through training data for empirical relationships tend to overfit the data	instance of	To reduce the risk of issues	0.80	text
meaning that they can identify	instance of	To reduce the risk of issues	0.80	text
exploit apparent relationships in the training data that do not hold in general.When a training set is continuously expanded with new data	instance of	To reduce the risk of issues	0.80	text
then this is incremental learning.Simplified example of training a neural network in object detection	instance of	To reduce the risk of issues	0.80	text
accuracy	instance of	the test data set is used to obtain the performance characteristics	0.80	text
sensitivity	instance of	the test data set is used to obtain the performance characteristics	0.80	text
specificity	instance of	the test data set is used to obtain the performance characteristics	0.80	text
F-measure	instance of	the test data set is used to obtain the performance characteristics	0.80	text
and so on	instance of	the test data set is used to obtain the performance characteristics	0.80	text

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Training data set

Overview

Validation data set

Test data set

Key facts & relationships

Topics to explore

Overview

Training data set

Validation data set

Test data set

Advanced semantic analysis

Map overview Semantic statistics