Min-max normalization
Min-max normalization is a feature-scaling technique that linearly maps numerical values to a specified interval using their minimum and maximum values.
Grokking AI Algorithms, Second Edition
Min-max scaling is a feature-scaling technique that linearly maps values between a specified minimum and maximum to a normalized range, commonly 0 to 1.
- How it is used
- Prepares differently sized input features so artificial neural networks compare them without bias from raw numerical magnitude.
- How deep it goes
- Explains feature-specific ranges, gives the scaling formula, and demonstrates a row-by-row Python implementation.
- Examples
- Uses speed, terrain quality, field of vision, driving experience, and collision labels from a car-collision dataset.
- What you can do after
- Apply min-max scaling by selecting feature bounds and transforming each dataset value with a short Python routine.
Read how Grokking AI Algorithms, Second Edition explains min-max normalization →
Outlier Detection in Python
A feature-scaling transformation that linearly maps values to a specified interval, commonly from zero to one.
- How it is used
- Prepares numeric features and outlier scores for distance calculations, detector combination, and comparable feature contributions.
- How deep it goes
- Defines the 0.0–1.0 transformation and demonstrates implementation, but defers detailed scaler treatment to later coverage.
- Examples
- Uses OpenML segment features, MAD scores, and baseball statistics to illustrate score and feature scaling.
- What you can do after
- Apply it via scikit-learn's MinMaxScaler to comparable numeric features or detector scores before distance calculations or aggregation.
Read how Outlier Detection in Python explains min-max normalization →
Math and Architectures of Deep Learning
A preprocessing method that rescales values into a fixed range, here 0–1.
- How it is used
- Prepares differently scaled features and targets before neural-network regression training.
- How deep it goes
- Defines the column-wise formula and shows its PyTorch implementation, stopping short of discussing edge cases or inverse scaling.
- Examples
- Uses demand prediction with sales, discount, holiday timing, and units-sold data.
- What you can do after
- Apply it via PyTorch by computing column extrema and transforming input and target tensors to 0–1.
Read how Math and Architectures of Deep Learning explains min-max normalization →
The Complete Obsolete Guide to Generative AI
Min-max scaling is a normalization method that linearly transforms values into a specified range, commonly from 0 to 1.
- How it is used
- Supports fairer comparisons and visualizations when features use different units or scales.
- How deep it goes
- Defines the formula and demonstrates a plotted workflow, but stops short of deeper algorithmic internals.
- Examples
- Uses CPU, memory, storage, employee salaries, experience, and server specifications as normalization examples.
- What you can do after
- Apply it via scikit-learn's MinMaxScaler to normalize tabular columns and plot comparable time-series trends.
Read how The Complete Obsolete Guide to Generative AI explains min-max normalization →
Related concepts
- normalizationused by · Normalization is the process of transforming a value, representation, or data structure to satisfy a specified standard, scale, or structural constraint.
- predict_probaused by · predict_proba is a classifier method that returns estimated probabilities for each possible class for supplied input samples.
- feature scalingimplements · Feature scaling is the transformation of numerical features to comparable magnitudes, ranges, or distributions for computation or model fitting.
- anomaly scoreuses · An anomaly score is a numerical measure of how anomalous an observation or event is relative to a reference or expected pattern.
- scikit-learnpart of · scikit-learn is an open-source Python library providing tools for classical machine learning, including algorithms, preprocessing, model selection, and evaluation.
- RobustScaleralternative to · RobustScaler is a scikit-learn transformer that scales features using robust statistics such as the median and interquartile range.
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