In digital circuits and machine learning, a one-hot is a group of bits among which the legal combinations of values are only those with a single high (1) bit and all the others low (0). A similar implementation in which all bits are '1' except one '0' is sometimes called one-cold. In statistics, dummy variables represent a similar technique for representing categorical data.
HINT:
👉 One-hot in the context of Simple encoder
An encoder (or "simple encoder") in digital electronics is a one-hot to binary converter. That is, if there are 2 input lines, and at most only one of them will ever be high, the binary code of this 'hot' line is produced on the n-bit output lines. A binary encoder is the dual of a binary decoder.
If the input circuit can guarantee at most a single-active input, a simple encoder is a better choice than a priority encoder, since it requires less logic to implement. However, a simple encoder can generate an incorrect output when more than a single input is active, so a priority encoder is required in such cases.
In this Dossier
One-hot in the context of Data preprocessing
Data preprocessing can refer to manipulation, filtration or augmentation of data before it is analyzed, and is often an important step in the data mining process. Data collection methods are often loosely controlled, resulting in out-of-range values, impossible data combinations, and missing values, amongst other issues.Preprocessing is the process by which unstructured data is transformed into intelligible representations suitable for machine-learning models. This phase of model deals with noise in order to arrive at better and improved results from the original data set which was noisy. This dataset also has some level of missing value present in it.
The preprocessing pipeline used can often have large effects on the conclusions drawn from the downstream analysis. Thus, representation and quality of data is necessary before running any analysis. Often, data preprocessing is the most important phase of a machine learning project, especially in computational biology. If there is a high proportion of irrelevant and redundant information present or noisy and unreliable data, then knowledge discovery during the training phase may be more difficult. Data preparation and filtering steps can take a considerable amount of processing time. Examples of methods used in data preprocessing include cleaning, instance selection, normalization, one-hot encoding, data transformation, feature extraction and feature selection.
View the full Wikipedia page for Data preprocessingOne-hot in the context of Features (pattern recognition)
In machine learning and pattern recognition, a feature is an individual measurable property or characteristic of a data set. Choosing informative, discriminating, and independent features is crucial to producing effective algorithms for pattern recognition, classification, and regression tasks. Features are usually numeric, but other types such as strings and graphs are used in syntactic pattern recognition, after some pre-processing step such as one-hot encoding. The concept of "features" is related to that of explanatory variables used in statistical techniques such as linear regression.
View the full Wikipedia page for Features (pattern recognition)