One-hot
Decimal | Binary | Unary | One-hot |
---|---|---|---|
0 | 000 | 00000000 | 00000001 |
1 | 001 | 00000001 | 00000010 |
2 | 010 | 00000011 | 00000100 |
3 | 011 | 00000111 | 00001000 |
4 | 100 | 00001111 | 00010000 |
5 | 101 | 00011111 | 00100000 |
6 | 110 | 00111111 | 01000000 |
7 | 111 | 01111111 | 10000000 |
In
Applications
Digital circuitry
One-hot encoding is often used for indicating the state of a
is needed to determine the state. A one-hot state machine, however, does not need a decoder as the state machine is in the nth state if, and only if, the nth bit is high.A
An address decoder converts from binary to one-hot representation. A priority encoder converts from one-hot representation to binary.
Comparison with other encoding methods
Advantages
- Determining the state has a low and constant cost of accessing one flip-flop
- Changing the state has the constant cost of accessing two flip-flops
- Easy to design and modify
- Easy to detect illegal states
- Takes advantage of an FPGA's abundant flip-flops
- Using a one-hot implementation typically allows a state machine to run at a faster clock rate than any other encoding of that state machine[3]
Disadvantages
- Requires more flip-flops than other encodings, making it impractical for PAL devices
- Many of the states are illegal[4]
Natural language processing
In natural language processing, a one-hot vector is a 1 × N matrix (vector) used to distinguish each word in a vocabulary from every other word in the vocabulary.[5] The vector consists of 0s in all cells with the exception of a single 1 in a cell used uniquely to identify the word. One-hot encoding ensures that machine learning does not assume that higher numbers are more important. For example, the value '8' is bigger than the value '1', but that does not make '8' more important than '1'. The same is true for words: the value 'laughter' is not more important than 'laugh'.
Machine learning and statistics
In machine learning, one-hot encoding is a frequently used method to deal with categorical data. Because many machine learning models need their input variables to be numeric, categorical variables need to be transformed in the pre-processing part. [6]
Food Name | Categorical # | Calories |
---|---|---|
Apple | 1 | 95 |
Chicken | 2 | 231 |
Broccoli | 3 | 50 |
Apple | Chicken | Broccoli | Calories |
---|---|---|---|
1 | 0 | 0 | 95 |
0 | 1 | 0 | 231 |
0 | 0 | 1 | 50 |
Categorical data can be either nominal or ordinal.[7] Ordinal data has a ranked order for its values and can therefore be converted to numerical data through ordinal encoding.[8] An example of ordinal data would be the ratings on a test ranging from A to F, which could be ranked using numbers from 6 to 1. Since there is no quantitative relationship between nominal variables' individual values, using ordinal encoding can potentially create a fictional ordinal relationship in the data.[9] Therefore, one-hot encoding is often applied to nominal variables, in order to improve the performance of the algorithm.
For each unique value in the original categorical column, a new column is created in this method. These dummy variables are then filled up with zeros and ones (1 meaning TRUE, 0 meaning FALSE).[10]
Because this process creates multiple new variables, it is prone to creating a 'big p' problem (too many predictors) if there are many unique values in the original column. Another downside of one-hot encoding is that it causes multicollinearity between the individual variables, which potentially reduces the model's accuracy.[11]
Also, if the categorical variable is an output variable, you may want to convert the values back into a categorical form in order to present them in your application.[12]
In practical usage, this transformation is often directly performed by a function that takes categorical data as an input and outputs the corresponding dummy variables. An example would be the dummyVars function of the Caret library in R.[13]
See also
- Bi-quinary coded decimal – Numeral encoding scheme
- Binary decoder – Combinational logic circuit
- Gray code – Ordering of binary values, used for positioning and error correction
- Kronecker delta – Mathematical function of two variables; outputs 1 if they are equal, 0 otherwise
- Indicator vector
- Serial decimal – computer numeric representation is one in which ten bits are reserved for each digit
- Single-entry vector– Concept in mathematics
- Unary numeral system – Base-1 numeral system
- Uniqueness quantification – Logical property of being the one and only object satisfying a condition
- XOR gate – Logic gate
References
- ISBN 978-0-12-394424-5.)
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: CS1 maint: multiple names: authors list (link - )
- ^ Xilinx. "HDL Synthesis for FPGAs Design Guide". section 3.13: "Encoding State Machines". Appendix A: "Accelerate FPGA Macros with One-Hot Approach". 1995.
- ISBN 0-9705394-2-8.
- . Retrieved 2022-05-22.
- ^ Brownlee, Jason. (2017). "Why One-Hot Encode Data in Machine Learning?". Machinelearningmastery. https://machinelearningmastery.com/why-one-hot-encode-data-in-machine-learning/
- ^ Stevens, S. S. (1946). “On the Theory of Scales of Measurement”. Science, New Series, 103.2684, pp. 677–680. http://www.jstor.org/stable/1671815.
- ^ Brownlee, Jason. (2020). "Ordinal and One-Hot Encodings for Categorical Data". Machinelearningmastery. https://machinelearningmastery.com/one-hot-encoding-for-categorical-data//
- ^ Brownlee, Jason. (2020). "Ordinal and One-Hot Encodings for Categorical Data". Machinelearningmastery. https://machinelearningmastery.com/one-hot-encoding-for-categorical-data//
- ^ Dinesh, Yadav. (2019). "Categorical encoding using Label-Encoding and One-Hot-Encoder". Towards Data Science. https://towardsdatascience.com/categorical-encoding-using-label-encoding-and-one-hot-encoder-911ef77fb5bd
- ^ Andre, Ye. (2020). " Stop One-Hot Encoding Your Categorical Variables. ". Towards Data Science. https://towardsdatascience.com/stop-one-hot-encoding-your-categorical-variables-bbb0fba89809
- ^ Brownlee, Jason. (2017). "Why One-Hot Encode Data in Machine Learning?". Machinelearningmastery. https://machinelearningmastery.com/why-one-hot-encode-data-in-machine-learning/
- ^ Kuhn, Max. “dummyVars”. RDocumentation. https://www.rdocumentation.org/packages/caret/versions/6.0-86/topics/dummyVars