How deeply does Deep Learning with R, Third Edition cover missing value?

Defines the issue and places it in preprocessing, while deferring domain-specific handling techniques.

Missing value

As explained in Deep Learning with R, Third Edition

A missing value is an absent or unavailable data entry for an expected attribute of an instance.

A missing value is an absent or unavailable entry for an attribute that an instance is expected to contain. Before training, inspect the data for missing entries and address them during preprocessing so the inputs become suitable for a neural network. section 6.1.3 section 6.2.1

Use missing-value checks as part of a broader data-preparation workflow:

Explore the dataset before training; numerical features can be examined with histograms, while other data types should be inspected through representative samples. section 6.1.3
Treat missing-value handling as one step alongside vectorization and value normalization, rather than feeding raw data directly to the model. section 6.2.1
Include missing-value treatment in dataset curation, together with checking data quality, labels, and feature usefulness. section 5.4.1

A numerical dataset

Suppose a numerical dataset contains feature entries that are absent for some instances. Plotting feature distributions can reveal the issue; those entries must then be dealt with during data preparation before model training. section 6.1.3 section 6.2.1

How Deep Learning with R, Third Edition covers it

Cover of Deep Learning with R, Third Edition

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Also explained in

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The Well-Grounded Data AnalystA missing value is an absent or unavailable value for a field that is expected to contain data.
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Machine Learning for Tabular Data: XGBoost, Deep Learning, and AIMissing data is the absence of recorded or available values or cases in a dataset, often represented explicitly by a special marker.
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Starting Data Analytics with Generative AI and PythonA missing value is an absent or unavailable data value in a record or variable.
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Statistics Slam DunkA missing value or missing data item is an observation whose value is unavailable, unrecorded, or not applicable in a dataset.
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API Design PatternsA missing value is a value that is absent because its field is omitted rather than explicitly assigned.
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Pandas Workout: 200 exercises to make you a stronger data analystA missing or null value is an absent or unavailable observation or field in a dataset.
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Outlier Detection in PythonThe absence of an expected value or observation in a dataset.
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The Well-Grounded Rubyist, Fourth Editionnil is Ruby’s singleton object representing the absence of a meaningful value.
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100 Go Mistakes and How to Avoid ThemA null value is a database value representing the absence of a value rather than a value of the column's declared type.
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Fast Python: High performance techniques for large datasets
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PostgreSQL Mistakes and How to Avoid ThemA SQL NULL is a marker representing an absent, unknown, or inapplicable value that participates in three-valued logic rather than ordinary equality.
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Everyday Data VisualizationA missing data point is an expected observation or value that is absent from a dataset or visualization.
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The Art of CodeIn Java, null is the literal value denoting that a reference does not refer to an object.
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Financial AI in PracticeData values that are absent from a dataset for some records or fields.
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100 Java Mistakes and How to Avoid ThemThe null value is Java's special reference value denoting the absence of an object reference.
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Clojure: The Essential Referencenil is Clojure's null value, representing the absence of a value or a null reference.
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.NET in Action, Second EditionA null value indicates that a reference or nullable value does not refer to an object or contain a non-null value.
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F# in Action: A Revised Edition of Get Programming with F#Missing data is information that is absent, unavailable, or not provided for an expected field or value.
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Learn Go with Pocket-Sized Projectsnil is Go's predeclared zero-like value representing the absence of a valid reference, interface value, or other applicable value.
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Data Storytelling with Altair and AIA missing value is an absent or unrecorded value for an observation and variable in a dataset.
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Machine Learning System DesignMissing data consists of absent values, records, or fields that are expected in a dataset or input.
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Graph Algorithms for Data ScienceMissing data is information that should be present for an entity or observation but is absent from the dataset.
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Modern CA null pointer is a pointer value that represents no valid object or function address.
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Statistics Every Programmer NeedsA null value is a missing or undefined data value represented as having no usable value.
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Modern C, Third Editionnullptr is the C23 null pointer constant used to represent a null pointer value.
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Grokking AI Algorithms, Second EditionA missing value is an absent or unrecorded value for an attribute in a data record.
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Pro ASP.NET Core 7, Tenth EditionA null value represents the absence of an object reference or other value in a context where null is permitted.
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Delivering Machine Learning Projects: From design to deploymentA null value is a database value representing the absence of a recorded value rather than a measured numeric value.
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Grokking Machine LearningA missing value is an absent, unreadable, or otherwise unavailable entry in a dataset.
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Julia as a Second LanguageMissing data is information absent from a collected dataset because it was not recorded, was lost, or is otherwise unavailable.
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Grokking StatisticsMissing data consists of observations or variable values that are absent from a dataset when they are expected or needed.
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Kotlin in Action, 2EA null value represents the absence of an object or reference value in a program.
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The Quick Python Book, Fourth EditionA missing value is a data item for which the intended observation is unavailable, inapplicable, or not reported.
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Deep Learning with Python, Third EditionAn absent data field in a sample that must be handled explicitly during preprocessing.

Compare how 35 books explain missing value →

Related concepts

left outer joinproduced by · A left outer join returns every row from the left relation and matching rows from the right relation, representing unmatched right-side values as nulls.
primitive data typekind of · A primitive data type is a fundamental, language-provided type for representing a basic value, rather than a composite of multiple values.
imputationuses · Imputation is the replacement of missing or incomplete data with estimated, inferred, representative, or otherwise selected values.
atomkind of · An atom is an Elixir named constant whose text is stored in the runtime atom table and whose value is a reference to that text.
findproduced by · A find operation searches a collection for the first element or entry matching a value or predicate and returns it, or indicates that no match exists.

This explainer was generated by Manning's knowledge graph from the text of Deep Learning with R, Third Edition. The section links point to the parts of the book it draws on.