Live briefing
Everything about Feature scaling
Method used to normalize the range of independent variables
Feature scaling is a method used to normalize the range of independent variables or features of data. In data processing, it is also known as data normalization and is generally performed during the data preprocessing step. Wikipedia →
🧭 Start here: a 6-step reading path
- Read the overview: Feature scalingA 5-minute orientation
- Skim a recent paper: Frequency-chirprate synchrosqueezing-based scaling chirplet transform for wind turbine nonstationary fault feature time–frequency representationInfluential work since 2022
- Pick up a classic book: HadoopThe most-reprinted book on the subject
- Read the landmark paper: Optimizing epileptic seizure recognition performance with feature scaling and dropout layersCited 199 times
- Go sideways: Normalization (statistics)A closely related topic
- Go sideways: Root mean square deviationA closely related topic
📖 Definitions
No dictionary entry for this exact phrase.
🗂️ The subject at a glance
🏆 Landmark papers (most cited)
Optimizing epileptic seizure recognition performance with feature scaling and dropout layers
The impact of neglecting feature scaling in k-means clustering
Class dependent feature scaling method using naive Bayes classifier for text datamining
Influence of feature scaling on convergence of gradient iterative algorithm
Latent Feature Extraction for Process Data via Multidimensional Scaling
Multi-View Scaling Support Vector Machines for Classification and Feature Selection
Feature Scaling for Kernel Fisher Discriminant Analysis Using Leave-One-Out Cross Validation
Scaling of MOS technology to submicrometer feature sizes
🆕 Influential since 2022
Frequency-chirprate synchrosqueezing-based scaling chirplet transform for wind turbine nonstationary fault feature time–frequency representation
The Impact of Feature Scaling in Machine Learning: Effects on Regression and Classification Tasks
Enhancing malware detection with feature selection and scaling techniques using machine learning models
Geometric imbalanced deep learning with feature scaling and boundary sample mining
Chaining Zscore and feature scaling methods to improve neural networks for classification
📚 Books worth reading
HadoopTom White, Tom White · 2009
Scale Model LifeBruce Kimball · 2014- 📕Detecting small-scale topographic changes and relict geomorphic features on barrier islands using SAR · 1997
- 📕A geographic comparison of selected large-scale planetary surface featuresStephen Paul Meszaros · 1984
- 📕Complexity scales and licensing in phonologyEugeniusz Cyran · 2010
Scale model traction engine building, featuring "Minnie"Leonard Charlish Mason · 1971- 📕Feature Store for Machine LearningJayanth Kumar M. J · 2022
Data Analytics Using Splunk 9. xNadine Shillingford · 2023
Ranked by number of editions on Open Library, a rough measure of how lasting a book is.
🔗 Related topics
- Normalization (statistics) In statistics and applications of statistics, normalization can have a range of meanings. In the simplest cases, normalization of ratings means adjusting…
- Root mean square deviation The root mean square deviation (RMSD) or root mean square error (RMSE) is a frequently used measure of the distances between actual observed values and…
- Standard score In statistics, the standard score or z-score is the number of standard deviations by which the value of a raw score (i.e., an observed value or data point)…
- Normalization (machine learning) In machine learning, normalization is a statistical technique with various applications. There are two main forms of normalization, namely data normalization…
- Standard deviation In statistics, the standard deviation is a measure of the amount of variation of the values of a variable about its (arithmetic) average. A low standard…
- Weighted arithmetic mean The weighted arithmetic mean is similar to an ordinary arithmetic mean (the most common type of average), except that instead of each of the data points…
- Statistical dispersion In statistics, dispersion (also called variability, scatter, or spread) is the extent to which a distribution is stretched or squeezed. Common examples…
- Softmax function The softmax function, also known as softargmax or normalized exponential function, converts a tuple of K real numbers into a probability distribution over…