What is One Hot Design?
In the field of computer science and electronics, there are some methods, which are used to represent data in such a way, so that it can be used in machines and computers. These methods are called encoding schemes. One- hot encoding is one of those methods.
In this article, we will be discussing one-hot encoding, it’s working, the applications, etc.
One hot encoding is been used in the process of categorizing data variables so they can be used in machine learning algorithms to make some better predictions. So, what we do in one-hot encoding, is to convert each categorical value into a different column, and it gives a binary value, either 0 or 1 to each column. And each integer value represents a binary vector.
To understand it better let’s take a look at an example below:
|Type||AB_ one-hot||BC_ one-hot||CD_ one-hot|
To clear this, let’s take another example, think we have some values yellow and green. With the help of one-hot, we can give a numeric value to yellow as 0 and green as 1.
Here, once we have already given the numeric values, the next step is to create a binary vector, which shows the numeric values that we have given. Here, a vector will show 2 as the length, as we have filed 2 values.
Thus, it will show you [1,0] as the value of yellow in the binary vector, and the value of green will be shown as [0,1].
One-hot encoding is been widely used in many different applications, such as electronics, machine learning, digital circuits, and so on. Some of the most common applications where one-hot is been used are
1. Machine Learning:
Machine learning is a method that is used in data analysis. This process includes adapting models and allowing programs to learn organically. Technically it involves the structuring of algorithms, which adopts models in order and improves their quality to make predictions.
The idea of machine learning is mainly based on the interaction between computers and human languages. It includes speech recognition and understanding,
Some of the areas where machine learning is commonly used are insurance claim analysis, bioinformatics and medical diagnosis, image processing and pattern recognition, search engines, financial market analysis, etc.
Why One-Hot Encoding is Important in Machine Learning?
One hot encoding is a useful method and is used for the type of data that has no relation with one another. In machine learning algorithms the order of integers is preferred. In clear words, it reads the higher number as the better one, then the lower one.
Well in some cases, the given input data may not have the right order for categorical value, which can cause a problem in the performance and lead to an error in predictions.
So, to prevent these kinds of problems one-hot encoding plays a major role. To understand it more clearly, let’s see how we can convert the categorical data into numerical one.
By following the steps below, we can convert the categorical data into numerical data.
- In the first step, we assign each category value with a numeric value. Suppose, we have three values A, B, C. We can assign them as 1, 2, and 3.
- Now since they have no order or ranking, in the second step, we have to apply the one-hot encoding to the integers that we have applied. For this, we add a binary variable in the place of integer encoded variables.
- Since we have given the three categories in the example, here we are using three binary variables. Here, we place the value 1 as the binary code for each model and the value 0 for the other ones.
One-hot encoding can also be used in electronics, where, a voltage can be used to represent a value on an analog or digital output line.
As an example, logic gate circuits are made of a large network of interconnected “nodes” with digital inputs, which creates digital outputs. Which seems to be a better option to represent an output state without using any decoding.
3. Digital Circuitry:
One-hot encoding is been used in a variety of different digital circuits to represent their I/O values. As an example, it can be used in representing the state of a state machine. Like-wise, if another representation is chosen, such as Grey or Binary, a decoder is needed to identify the state. But with a one-hot, state-machine does not require the decoder, as the machine is logically in the nth state if the nth bit is high.
A ring counter can be considered a good example of a finite-state machine. Where the output of one flip-flop is connected to the input of the other.
The first counter represents the first state, and the second one shows the second state and is continuous in the same way. In the beginning, every flip-flop in the machine is set to ‘0’, other than the first, which shows the value ‘1’.
The one ‘hot’ bit is moved to the second flip flop, as the next clock edge arrives at the flip flops. The ‘hot’ bit is continuous in this way until the machine arrives at the last state after it returns to the first state.
- Suitable for machine learning: The first, and most important advantage of one-hot encoding is, it’s working. One hot encoding is intended to process the categorical data variables so they can be used in machine learning algorithms to make some better predictions.
- Convert data into binary values: One-hot is used to convert each categorical value into a different categorical column, and it gives a binary value, either 0 or 1, rather than ordinal.
- The simplicity of implementation: This method is simple and straight to implement. And also, easier to understand than the other methods.
- It takes less time decoding data: Using one-hot encoding is known to be a faster way, as it takes less time decoding the data compared to other methods.
- Increases the computational cost: It increases the computational cost, as during the processing it increases the number of dimensions.
- Representation of many values is difficult: Also, one more disadvantage which it has is, that it cannot represent many values. As an example, for n states, we will need n digits or flip-flops.
- The possibility of multi: Collinearity is higher- The other disadvantage of one-hot encoding is that, because of the dummy variables, the possibility of multicollinearity is much higher, and that can affect the performance of the model.
- Increase of sparsity: Sparse matrix is those, where the majority of elements are zero. increase in sparsity can be another disadvantage of one-hot encoding.