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<title>Feature (machine learning)</title>

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<span id="openzim-page-title" class="mw-page-title-main"><span class="mw-page-title-main">Feature (machine learning)</span></span>

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</style><div role="note" class="hatnote navigation-not-searchable">Not to be confused with <a href="Feature_(computer_vision)" title="Feature (computer vision)">Feature (computer vision)</a>.</div>

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</style><table class="sidebar sidebar-collapse nomobile nowraplinks"><tbody><tr><td class="sidebar-pretitle">Part of a series on</td></tr><tr><th class="sidebar-title-with-pretitle"><a href="Machine_learning" title="Machine learning">Machine learning</a><br>and <a href="Data_mining" title="Data mining">data mining</a></th></tr><tr><td class="sidebar-content">

<ul><li><a href="Supervised_learning" title="Supervised learning">Supervised learning</a></li>

<li><a href="Unsupervised_learning" title="Unsupervised learning">Unsupervised learning</a></li>

<li><a href="Semi-supervised_learning" class="mw-redirect" title="Semi-supervised learning">Semi-supervised learning</a></li>

<li><a href="Self-supervised_learning" title="Self-supervised learning">Self-supervised learning</a></li>

<li><a href="Reinforcement_learning" title="Reinforcement learning">Reinforcement learning</a></li>

<li><a href="Meta-learning_(computer_science)" title="Meta-learning (computer science)">Meta-learning</a></li>

<li><a href="Online_machine_learning" title="Online machine learning">Online learning</a></li>

<li><a href="Batch_learning" class="mw-redirect" title="Batch learning">Batch learning</a></li>

<li><a href="Curriculum_learning" title="Curriculum learning">Curriculum learning</a></li>

<li><a href="Rule-based_machine_learning" title="Rule-based machine learning">Rule-based learning</a></li>

<li><a href="Neuro-symbolic_AI" title="Neuro-symbolic AI">Neuro-symbolic AI</a></li>

<li><a href="Neuromorphic_engineering" class="mw-redirect" title="Neuromorphic engineering">Neuromorphic engineering</a></li>

<li><a href="Quantum_machine_learning" title="Quantum machine learning">Quantum machine learning</a></li></ul></div></div></td>

</tr><tr><td class="sidebar-content">

<ul><li><a href="Statistical_classification" title="Statistical classification">Classification</a></li>

<li><a href="Generative_model" title="Generative model">Generative modeling</a></li>

<li><a href="Regression_analysis" title="Regression analysis">Regression</a></li>

<li><a href="Cluster_analysis" title="Cluster analysis">Clustering</a></li>

<li><a href="Dimensionality_reduction" title="Dimensionality reduction">Dimensionality reduction</a></li>

<li><a href="Density_estimation" title="Density estimation">Density estimation</a></li>

<li><a href="Anomaly_detection" title="Anomaly detection">Anomaly detection</a></li>

<li><a href="Data_cleaning" class="mw-redirect" title="Data cleaning">Data cleaning</a></li>

<li><a href="Automated_machine_learning" title="Automated machine learning">AutoML</a></li>

<li><a href="Association_rule_learning" title="Association rule learning">Association rules</a></li>

<li><a href="Semantic_analysis_(machine_learning)" title="Semantic analysis (machine learning)">Semantic analysis</a></li>

<li><a href="Structured_prediction" title="Structured prediction">Structured prediction</a></li>

<li><a href="Feature_engineering" title="Feature engineering">Feature engineering</a></li>

<li><a href="Feature_learning" title="Feature learning">Feature learning</a></li>

<li><a href="Learning_to_rank" title="Learning to rank">Learning to rank</a></li>

<li><a href="Grammar_induction" title="Grammar induction">Grammar induction</a></li>

<li><a href="Ontology_learning" title="Ontology learning">Ontology learning</a></li>

<li><a href="Multimodal_learning" title="Multimodal learning">Multimodal learning</a></li></ul></div></div></td>

</tr><tr><td class="sidebar-content">

<div class="sidebar-list mw-collapsible mw-collapsed machine-learning-list-title"><div class="sidebar-list-title" style="border-top:1px solid #aaa; text-align:center;;color: var(--color-base)"><div style="display: inline-block; line-height: 1.2em; padding: .1em 0;"><a href="Supervised_learning" title="Supervised learning">Supervised learning</a><br><span class="nobold"><span style="font-size: 85%;">(<b><a href="Statistical_classification" title="Statistical classification">classification</a></b> • <b><a href="Regression_analysis" title="Regression analysis">regression</a></b>)</span></span> </div></div><div class="sidebar-list-content mw-collapsible-content hlist">

<ul><li><a href="Apprenticeship_learning" title="Apprenticeship learning">Apprenticeship learning</a></li>

<li><a href="Decision_tree_learning" title="Decision tree learning">Decision trees</a></li>

<li><a href="Ensemble_learning" title="Ensemble learning">Ensembles</a>

<ul><li><a href="Bootstrap_aggregating" title="Bootstrap aggregating">Bagging</a></li>

<li><a href="Boosting_(machine_learning)" title="Boosting (machine learning)">Boosting</a></li>

<li><a href="Random_forest" title="Random forest">Random forest</a></li></ul></li>

<li><a href="Linear_regression" title="Linear regression">Linear regression</a></li>

<li><a href="Naive_Bayes_classifier" title="Naive Bayes classifier">Naive Bayes</a></li>

<li><a href="Artificial_neural_network" class="mw-redirect" title="Artificial neural network">Artificial neural networks</a></li>

<li><a href="Logistic_regression" title="Logistic regression">Logistic regression</a></li>

<li><a href="Perceptron" title="Perceptron">Perceptron</a></li>

<li><a href="Relevance_vector_machine" title="Relevance vector machine">Relevance vector machine (RVM)</a></li>

<li><a href="Support_vector_machine" title="Support vector machine">Support vector machine (SVM)</a></li></ul></div></div></td>

</tr><tr><td class="sidebar-content">

<div class="sidebar-list mw-collapsible mw-collapsed machine-learning-list-title"><div class="sidebar-list-title" style="border-top:1px solid #aaa; text-align:center;;color: var(--color-base)"><a href="Cluster_analysis" title="Cluster analysis">Clustering</a></div><div class="sidebar-list-content mw-collapsible-content hlist">

<ul><li><a href="BIRCH" title="BIRCH">BIRCH</a></li>

<li><a href="Hierarchical_clustering" title="Hierarchical clustering">Hierarchical</a></li>

<li><a href="K-means_clustering" title="K-means clustering"><i>k</i>-means</a></li>

<li><a href="Fuzzy_clustering" title="Fuzzy clustering">Fuzzy</a></li>

<li><a href="Expectation%E2%80%93maximization_algorithm" title="Expectation–maximization algorithm">Expectation–maximization (EM)</a></li>

<li><br><a href="DBSCAN" title="DBSCAN">DBSCAN</a></li>

<li><a href="OPTICS_algorithm" title="OPTICS algorithm">OPTICS</a></li>

<li><a href="Mean_shift" title="Mean shift">Mean shift</a></li></ul></div></div></td>

</tr><tr><td class="sidebar-content">

<div class="sidebar-list mw-collapsible mw-collapsed machine-learning-list-title"><div class="sidebar-list-title" style="border-top:1px solid #aaa; text-align:center;;color: var(--color-base)"><a href="Dimensionality_reduction" title="Dimensionality reduction">Dimensionality reduction</a></div><div class="sidebar-list-content mw-collapsible-content hlist">

<ul><li><a href="Factor_analysis" title="Factor analysis">Factor analysis</a></li>

</tr><tr><td class="sidebar-content">

<div class="sidebar-list mw-collapsible mw-collapsed machine-learning-list-title"><div class="sidebar-list-title" style="border-top:1px solid #aaa; text-align:center;;color: var(--color-base)"><a href="Structured_prediction" title="Structured prediction">Structured prediction</a></div><div class="sidebar-list-content mw-collapsible-content hlist">

<ul><li><a href="Graphical_model" title="Graphical model">Graphical models</a>

<ul><li><a href="Bayesian_network" title="Bayesian network">Bayes net</a></li>

<li><a href="Conditional_random_field" title="Conditional random field">Conditional random field</a></li>

<li><a href="Hidden_Markov_model" title="Hidden Markov model">Hidden Markov</a></li></ul></li></ul></div></div></td>

</tr><tr><td class="sidebar-content">

<div class="sidebar-list mw-collapsible mw-collapsed machine-learning-list-title"><div class="sidebar-list-title" style="border-top:1px solid #aaa; text-align:center;;color: var(--color-base)"><a href="Anomaly_detection" title="Anomaly detection">Anomaly detection</a></div><div class="sidebar-list-content mw-collapsible-content hlist">

<ul><li><a href="Random_sample_consensus" title="Random sample consensus">RANSAC</a></li>

<li><a href="Local_outlier_factor" title="Local outlier factor">Local outlier factor</a></li>

<li><a href="Isolation_forest" title="Isolation forest">Isolation forest</a></li></ul></div></div></td>

</tr><tr><td class="sidebar-content">

<div class="sidebar-list mw-collapsible mw-collapsed machine-learning-list-title"><div class="sidebar-list-title" style="border-top:1px solid #aaa; text-align:center;;color: var(--color-base)"><a href="Neural_network_(machine_learning)" title="Neural network (machine learning)">Neural networks</a></div><div class="sidebar-list-content mw-collapsible-content hlist">

<ul><li><a href="Autoencoder" title="Autoencoder">Autoencoder</a></li>

<li><a href="Deep_learning" title="Deep learning">Deep learning</a></li>

<li><a href="Feedforward_neural_network" title="Feedforward neural network">Feedforward neural network</a></li>

<li><a href="Recurrent_neural_network" title="Recurrent neural network">Recurrent neural network</a>

<li><a href="Reservoir_computing" title="Reservoir computing">reservoir computing</a></li></ul></li>

<li><a href="Boltzmann_machine" title="Boltzmann machine">Boltzmann machine</a>

<ul><li><a href="Restricted_Boltzmann_machine" title="Restricted Boltzmann machine">Restricted</a></li></ul></li>

<li><a href="Diffusion_model" title="Diffusion model">Diffusion model</a></li>

<li><a href="Convolutional_neural_network" title="Convolutional neural network">Convolutional neural network</a>

<li><a href="LeNet" title="LeNet">LeNet</a></li>

<li><a href="AlexNet" title="AlexNet">AlexNet</a></li>

<li><a href="DeepDream" title="DeepDream">DeepDream</a></li></ul></li>

<li><a href="Neural_field" title="Neural field">Neural field</a>

<ul><li><a href="Neural_radiance_field" title="Neural radiance field">Neural radiance field</a></li>

<li><a href="Physics-informed_neural_networks" title="Physics-informed neural networks">Physics-informed neural networks</a></li></ul></li>

<li><a href="Transformer_(deep_learning_architecture)" title="Transformer (deep learning architecture)">Transformer</a>

<ul><li><a href="Vision_transformer" title="Vision transformer">Vision</a></li></ul></li>

<li><a href="Mamba_(deep_learning_architecture)" title="Mamba (deep learning architecture)">Mamba</a></li>

<li><a href="Spiking_neural_network" title="Spiking neural network">Spiking neural network</a></li>

<li><a href="Memtransistor" title="Memtransistor">Memtransistor</a></li>

<li><a href="Electrochemical_RAM" title="Electrochemical RAM">Electrochemical RAM</a> (ECRAM)</li></ul></div></div></td>

</tr><tr><td class="sidebar-content">

<div class="sidebar-list mw-collapsible mw-collapsed machine-learning-list-title"><div class="sidebar-list-title" style="border-top:1px solid #aaa; text-align:center;;color: var(--color-base)"><a href="Reinforcement_learning" title="Reinforcement learning">Reinforcement learning</a></div><div class="sidebar-list-content mw-collapsible-content hlist">

<ul><li><a href="Q-learning" title="Q-learning">Q-learning</a></li>

<li><a href="Policy_gradient_method" title="Policy gradient method">Policy gradient</a></li>

<li><a href="State%E2%80%93action%E2%80%93reward%E2%80%93state%E2%80%93action" title="State–action–reward–state–action">SARSA</a></li>

<li><a href="Temporal_difference_learning" title="Temporal difference learning">Temporal difference (TD)</a></li>

<li><a href="Multi-agent_reinforcement_learning" title="Multi-agent reinforcement learning">Multi-agent</a>

</tr><tr><td class="sidebar-content">

<ul><li><a href="Active_learning_(machine_learning)" title="Active learning (machine learning)">Active learning</a></li>

<li><a href="Crowdsourcing" title="Crowdsourcing">Crowdsourcing</a></li>

<li><a href="Human-in-the-loop" title="Human-in-the-loop">Human-in-the-loop</a></li>

<li><a href="Mechanistic_interpretability" title="Mechanistic interpretability">Mechanistic interpretability</a></li>

</tr><tr><td class="sidebar-content">

<ul><li><a href="Coefficient_of_determination" title="Coefficient of determination">Coefficient of determination</a></li>

<li><a href="Confusion_matrix" title="Confusion matrix">Confusion matrix</a></li>

<li><a href="Learning_curve_(machine_learning)" title="Learning curve (machine learning)">Learning curve</a></li>

<li><a href="Receiver_operating_characteristic" title="Receiver operating characteristic">ROC curve</a></li></ul></div></div></td>

</tr><tr><td class="sidebar-content">

<div class="sidebar-list mw-collapsible mw-collapsed machine-learning-list-title"><div class="sidebar-list-title" style="border-top:1px solid #aaa; text-align:center;;color: var(--color-base)">Mathematical foundations</div><div class="sidebar-list-content mw-collapsible-content hlist">

<ul><li><a href="Kernel_machines" class="mw-redirect" title="Kernel machines">Kernel machines</a></li>

<li><a href="Bias%E2%80%93variance_tradeoff" title="Bias–variance tradeoff">Bias–variance tradeoff</a></li>

<li><a href="Computational_learning_theory" title="Computational learning theory">Computational learning theory</a></li>

<li><a href="Empirical_risk_minimization" title="Empirical risk minimization">Empirical risk minimization</a></li>

<li><a href="Occam_learning" title="Occam learning">Occam learning</a></li>

<li><a href="Probably_approximately_correct_learning" title="Probably approximately correct learning">PAC learning</a></li>

<li><a href="Statistical_learning_theory" title="Statistical learning theory">Statistical learning</a></li>

<li><a href="Vapnik%E2%80%93Chervonenkis_theory" title="Vapnik–Chervonenkis theory">VC theory</a></li>

<li><a href="Topological_deep_learning" title="Topological deep learning">Topological deep learning</a></li></ul></div></div></td>

</tr><tr><td class="sidebar-content">

<div class="sidebar-list mw-collapsible mw-collapsed machine-learning-list-title"><div class="sidebar-list-title" style="border-top:1px solid #aaa; text-align:center;;color: var(--color-base)">Journals and conferences</div><div class="sidebar-list-content mw-collapsible-content hlist">

<li><a href="Conference_on_Neural_Information_Processing_Systems" title="Conference on Neural Information Processing Systems">NeurIPS</a></li>

<li><a href="International_Joint_Conference_on_Artificial_Intelligence" title="International Joint Conference on Artificial Intelligence">IJCAI</a></li>

</tr><tr><td class="sidebar-content">

<ul><li><a href="Glossary_of_artificial_intelligence" title="Glossary of artificial intelligence">Glossary of artificial intelligence</a></li>

<li><a href="List_of_datasets_for_machine-learning_research" title="List of datasets for machine-learning research">List of datasets for machine-learning research</a>

<ul><li><a href="List_of_datasets_in_computer_vision_and_image_processing" title="List of datasets in computer vision and image processing">List of datasets in computer vision and image processing</a></li></ul></li>

<li><a href="Outline_of_machine_learning" title="Outline of machine learning">Outline of machine learning</a></li></ul></div></div></td>

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<p>In <a href="Machine_learning" title="Machine learning">machine learning</a> and <a href="Pattern_recognition" title="Pattern recognition">pattern recognition</a>, a <b>feature</b> is an individual measurable property or characteristic of a data set.<sup id="cite_ref-ml_1-0" class="reference"><a href="#cite_note-ml-1"><span class="cite-bracket">[</span>1<span class="cite-bracket">]</span></a></sup> Choosing informative, discriminating, and independent features is crucial to produce effective <a href="Algorithm" title="Algorithm">algorithms</a> for <a href="Pattern_recognition" title="Pattern recognition">pattern recognition</a>, <a href="Classification_(machine_learning)" class="mw-redirect" title="Classification (machine learning)">classification</a>, and <a href="Regression_analysis" title="Regression analysis">regression</a> tasks. Features are usually numeric, but other types such as <a href="String_(computer_science)" title="String (computer science)">strings</a> and <a href="Graph_(discrete_mathematics)" title="Graph (discrete mathematics)">graphs</a> are used in <a href="Syntactic_pattern_recognition" title="Syntactic pattern recognition">syntactic pattern recognition</a>, after some pre-processing step such as <a href="One-hot" title="One-hot">one-hot encoding</a>. The concept of "features" is related to that of <a href="Dependent_and_independent_variables" title="Dependent and independent variables">explanatory variables</a> used in statistical techniques such as <a href="Linear_regression" title="Linear regression">linear regression</a>.

</p>

<div class="mw-heading mw-heading2"><h2 id="Feature_types">Feature types</h2></div>

<p>In feature engineering, two types of features are commonly used: numerical and categorical.

</p><p>Numerical features are continuous values that can be measured on a scale. Examples of numerical features include age, height, weight, and income. Numerical features can be used in machine learning algorithms directly.

</p><p><a href="Categorical_variable" title="Categorical variable">Categorical features</a> are discrete values that can be grouped into categories. Examples of categorical features include gender, color, and zip code. Categorical features typically need to be converted to numerical features before they can be used in machine learning algorithms. This can be done using a variety of techniques, such as one-hot encoding, label encoding, and ordinal encoding.

</p><p>The type of feature that is used in feature engineering depends on the specific machine learning algorithm that is being used. Some machine learning algorithms, such as decision trees, can handle both numerical and categorical features. Other machine learning algorithms, such as linear regression, can only handle numerical features.

</p>

<div class="mw-heading mw-heading2"><h2 id="Classification">Classification</h2></div>

<p>A numeric feature can be conveniently described by a feature vector. One way to achieve <a href="Binary_classification" title="Binary classification">binary classification</a> is using a <a href="Linear_predictor_function" title="Linear predictor function">linear predictor function</a> (related to the <a href="Perceptron" title="Perceptron">perceptron</a>) with a feature vector as input. The method consists of calculating the <a href="Dot_product" title="Dot product">scalar product</a> between the feature vector and a vector of weights, qualifying those observations whose result exceeds a threshold.

</p><p>Algorithms for classification from a feature vector include <a href="K-nearest_neighbor_algorithm" class="mw-redirect" title="K-nearest neighbor algorithm">nearest neighbor classification</a>, <a href="Artificial_neural_network" class="mw-redirect" title="Artificial neural network">neural networks</a>, and <a href="Statistical_classification" title="Statistical classification">statistical techniques</a> such as <a href="Bayesian_inference" title="Bayesian inference">Bayesian approaches</a>.

</p>

<div class="mw-heading mw-heading2"><h2 id="Examples">Examples</h2></div>

<div role="note" class="hatnote navigation-not-searchable">See also: <a href="Feature_(computer_vision)" title="Feature (computer vision)">Feature (computer vision)</a></div>

<p>In <a href="Character_recognition" class="mw-redirect" title="Character recognition">character recognition</a>, features may include <a href="Histogram" title="Histogram">histograms</a> counting the number of black pixels along horizontal and vertical directions, number of internal holes, stroke detection and many others.

</p><p>In <a href="Speech_recognition" title="Speech recognition">speech recognition</a>, features for recognizing <a href="Phonemes" class="mw-redirect" title="Phonemes">phonemes</a> can include noise ratios, length of sounds, relative power, filter matches and many others.

</p><p>In <a href="Spam_(electronic)" class="mw-redirect" title="Spam (electronic)">spam</a> detection algorithms, features may include the presence or absence of certain email headers,

the email structure, the language, the frequency of specific terms, the grammatical correctness of the text.

</p><p>In <a href="Computer_vision" title="Computer vision">computer vision</a>, there are a large number of possible <a href="Feature_(computer_vision)" title="Feature (computer vision)">features</a>, such as edges and objects.

</p>

<div class="mw-heading mw-heading2"><h2 id="Feature_vectors">Feature vectors</h2></div>

<div role="note" class="hatnote navigation-not-searchable">See also: <a href="Word_embedding" title="Word embedding">Word embedding</a></div>

<div role="note" class="hatnote navigation-not-searchable">"Feature space" redirects here. For feature spaces in kernel machines, see <a href="Kernel_method" title="Kernel method">Kernel method</a>.</div>

<p>In <a href="Pattern_recognition" title="Pattern recognition">pattern recognition</a> and <a href="Machine_learning" title="Machine learning">machine learning</a>, a <b>feature vector</b> is an n-dimensional <a href="Vector_(mathematics_and_physics)" title="Vector (mathematics and physics)">vector</a> of numerical features that represent some object. Many <a href="Algorithm" title="Algorithm">algorithms</a> in machine learning require a numerical representation of objects, since such representations facilitate processing and statistical analysis. When representing images, the feature values might correspond to the pixels of an image, while when representing texts the features might be the frequencies of occurrence of textual terms. Feature vectors are equivalent to the vectors of <a href="Explanatory_variable" class="mw-redirect" title="Explanatory variable">explanatory variables</a> used in <a href="Statistics" title="Statistics">statistical</a> procedures such as <a href="Linear_regression" title="Linear regression">linear regression</a>. Feature vectors are often combined with weights using a <a href="Dot_product" title="Dot product">dot product</a> in order to construct a <a href="Linear_predictor_function" title="Linear predictor function">linear predictor function</a> that is used to determine a score for making a prediction.

</p><p>The <a href="Vector_space" title="Vector space">vector space</a> associated with these vectors is often called the <b>feature space</b>. In order to reduce the dimensionality of the feature space, a number of <a href="Dimensionality_reduction" title="Dimensionality reduction">dimensionality reduction</a> techniques can be employed.

</p><p>Higher-level features can be obtained from already available features and added to the feature vector; for example, for the study of diseases the feature 'Age' is useful and is defined as <i>Age = 'Year of death' minus 'Year of birth' </i>. This process is referred to as <b>feature construction</b>.<sup id="cite_ref-Liu1998_2-0" class="reference"><a href="#cite_note-Liu1998-2"><span class="cite-bracket">[</span>2<span class="cite-bracket">]</span></a></sup><sup id="cite_ref-Piramithu2009_3-0" class="reference"><a href="#cite_note-Piramithu2009-3"><span class="cite-bracket">[</span>3<span class="cite-bracket">]</span></a></sup> Feature construction is the application of a set of constructive operators to a set of existing features resulting in construction of new features. Examples of such constructive operators include checking for the equality conditions {=, ≠}, the arithmetic operators {+,−,×, /}, the array operators {max(S), min(S), average(S)} as well as other more sophisticated operators, for example count(S,C)<sup id="cite_ref-bloedorn1998_4-0" class="reference"><a href="#cite_note-bloedorn1998-4"><span class="cite-bracket">[</span>4<span class="cite-bracket">]</span></a></sup> that counts the number of features in the feature vector S satisfying some condition C or, for example, distances to other recognition classes generalized by some accepting device. Feature construction has long been considered a powerful tool for increasing both accuracy and understanding of structure, particularly in high-dimensional problems.<sup id="cite_ref-breinman1984_5-0" class="reference"><a href="#cite_note-breinman1984-5"><span class="cite-bracket">[</span>5<span class="cite-bracket">]</span></a></sup> Applications include studies of disease and <a href="Emotion_recognition" title="Emotion recognition">emotion recognition</a> from speech.<sup id="cite_ref-Sidorova2009_6-0" class="reference"><a href="#cite_note-Sidorova2009-6"><span class="cite-bracket">[</span>6<span class="cite-bracket">]</span></a></sup>

</p>

<div class="mw-heading mw-heading2"><h2 id="Selection_and_extraction">Selection and extraction</h2></div>

<div role="note" class="hatnote navigation-not-searchable">Main articles: <a href="Feature_selection" title="Feature selection">Feature selection</a> and <a href="Feature_extraction" class="mw-redirect" title="Feature extraction">Feature extraction</a></div>

<p>The initial set of raw features can be redundant and large enough that estimation and optimization is made difficult or ineffective. Therefore, a preliminary step in many applications of <a href="Machine_learning" title="Machine learning">machine learning</a> and <a href="Pattern_recognition" title="Pattern recognition">pattern recognition</a> consists of <a href="Feature_selection" title="Feature selection">selecting</a> a subset of features, or <a href="Feature_extraction" class="mw-redirect" title="Feature extraction">constructing</a> a new and reduced set of features to facilitate learning, and to improve generalization and interpretability.<sup id="cite_ref-7" class="reference"><a href="#cite_note-7"><span class="cite-bracket">[</span>7<span class="cite-bracket">]</span></a></sup>

</p><p>Extracting or selecting features is a combination of art and science; developing systems to do so is known as <a href="Feature_engineering" title="Feature engineering">feature engineering</a>. It requires the experimentation of multiple possibilities and the combination of automated techniques with the intuition and knowledge of the <a href="Domain_expert" class="mw-redirect" title="Domain expert">domain expert</a>. Automating this process is <a href="Feature_learning" title="Feature learning">feature learning</a>, where a machine not only uses features for learning, but learns the features itself.

</p>

<ul><li><a href="Covariate" class="mw-redirect" title="Covariate">Covariate</a></li>

<li><a href="Dimensionality_reduction" title="Dimensionality reduction">Dimensionality reduction</a></li>

<li><a href="Feature_engineering" title="Feature engineering">Feature engineering</a></li>

<li><a href="Hashing_trick" class="mw-redirect" title="Hashing trick">Hashing trick</a></li>

<li><a href="Statistical_classification" title="Statistical classification">Statistical classification</a></li>

<li><a href="Explainable_artificial_intelligence" title="Explainable artificial intelligence">Explainable artificial intelligence</a></li></ul>

<div class="mw-heading mw-heading2"><h2 id="References">References</h2></div>

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</style><cite id="CITEREFBishop,_Christopher2006" class="citation book cs1">Bishop, Christopher (2006). <i>Pattern recognition and machine learning</i>. Berlin: Springer. <a href="ISBN_(identifier)" class="mw-redirect" title="ISBN (identifier)">ISBN</a> <bdi>0-387-31073-8</bdi>.</cite></span>

</li>

<li id="cite_note-Liu1998-2"><span class="mw-cite-backlink"><b><a href="#cite_ref-Liu1998_2-0">^</a></b></span> <span class="reference-text">Liu, H., Motoda H. (1998) <i><a rel="nofollow" class="external text" href="https://books.google.com/books?id=aaDbBwAAQBAJ">Feature Selection for Knowledge Discovery and Data Mining</a>.</i>, Kluwer Academic Publishers. Norwell, MA, USA. 1998.</span>

</li>

<li id="cite_note-Piramithu2009-3"><span class="mw-cite-backlink"><b><a href="#cite_ref-Piramithu2009_3-0">^</a></b></span> <span class="reference-text">Piramuthu, S., Sikora R. T. <a rel="nofollow" class="external text" href="https://www.sciencedirect.com/science/article/pii/S0957417408001309">Iterative feature construction for improving inductive learning algorithms</a>. In Journal of Expert Systems with Applications. Vol. 36 , Iss. 2 (March 2009), pp. 3401-3406, 2009</span>

</li>

<li id="cite_note-bloedorn1998-4"><span class="mw-cite-backlink"><b><a href="#cite_ref-bloedorn1998_4-0">^</a></b></span> <span class="reference-text">Bloedorn, E., Michalski, R. Data-driven constructive induction: a methodology and its applications. IEEE Intelligent Systems, Special issue on Feature Transformation and Subset Selection, pp. 30-37, March/April, 1998</span>

</li>

<li id="cite_note-breinman1984-5"><span class="mw-cite-backlink"><b><a href="#cite_ref-breinman1984_5-0">^</a></b></span> <span class="reference-text">Breiman, L. Friedman, T., Olshen, R., Stone, C. (1984) <i>Classification and regression trees</i>, Wadsworth</span>

</li>

<li id="cite_note-Sidorova2009-6"><span class="mw-cite-backlink"><b><a href="#cite_ref-Sidorova2009_6-0">^</a></b></span> <span class="reference-text">Sidorova, J., Badia T. <a rel="nofollow" class="external text" href="https://ieeexplore.ieee.org/abstract/document/5402574/">Syntactic learning for ESEDA.1, tool for enhanced speech emotion detection and analysis</a>. Internet Technology and Secured Transactions Conference 2009 (ICITST-2009), London, November 9–12. IEEE</span>

</li>

<li id="cite_note-7"><span class="mw-cite-backlink"><b><a href="#cite_ref-7">^</a></b></span> <span class="reference-text"><cite id="CITEREFHastieTibshiraniFriedman2009" class="citation book cs1">Hastie, Trevor; Tibshirani, Robert; Friedman, Jerome H. (2009). <a rel="nofollow" class="external text" href="https://books.google.com/books?id=eBSgoAEACAAJ"><i>The Elements of Statistical Learning: Data Mining, Inference, and Prediction</i></a>. Springer. <a href="ISBN_(identifier)" class="mw-redirect" title="ISBN (identifier)">ISBN</a> <bdi>978-0-387-84884-6</bdi>.</cite></span>

</li>

</ol></div></div></div><div><div class="zim-footer">

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