The machine learning models deployed in numerous applications often require a series of conversions from categorical data or the text foci to the numeric description. To comply with conversion needs two types of encoders are used namely label encoders and one hot encoder.
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The tricky part is when to choose label encoder and when to choose one hot encoder. The choice of decision impacts the model and also forms the basics of many questions generally asked for data scientists and machine learning enthusiasts.
The choice of encoding vividly affects the accuracy quotient of the model and, hence can lead to an optimized solution. To understand the difference it will make on models, we need to understand label encoders and one hot encoder.
Through a knowledge graph in Artificial Intelligence and Machine Learning, one aspect that most of us would recognize is that most of the algorithms task reasonably with numerical inputs. Accordingly, the central challenge confronted by an analyst is to transform text data into numerical data and nonetheless make a model formulate a point out of it.
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Label Encoding cites the transmogrification of the labels into the numeric form to change it into a form that can be read by the machine. Machine learning algorithms can thereafter determine in a correct way as to how these labels must be managed. It is a crucial pre-processing measure during the integrated dataset in supervised learning.
For example, we have a dataset that has a comparison of a certain quality in a certain skill in the form of a superlative comparison between siblings. The dataset is good, better, best. After applying a label encoder each quality will be given a label 0,1,2 respectively. The label for good quality is 0, for better the label is 1, and for best quality, the label is 2.
The above-mentioned example was basic in terms of the dataset. The conversion can be of any dataset be it of height, age, eye colour, iris type, symptoms, etc.
Label Encoding in Python can be implemented using the Sklearn Library. Sklearn furnishes a very effective method for encoding the categories of categorical features into numeric values. Label encoder encodes labels with credit between 0 and n-1 classes where n is the number of diverse labels. If a label reiterates it appoints the exact merit to as appointed before.
And to renovate this type of categorical text data into data that can be understood by model numerical data, we use the Label Encoder class. We need to label encode the initial column, import the LabelEncoder class from the sklearn library, equip and revamp the initial section of the data, and then rehabilitate the occurring text data with the fresh encoded data.
This is a brief description of label encoding. Hinging on the data, label encoding initiates a new dilemma. For illustration, we have encoded a bunch of kingdom names into numerical data. This is entirely categorical data and there is no association, of any means, between the rows.
To resolve this obstacle there exists a need to adopt a new technique of encoding. The dilemma here is since there are several quantities in a similar section, the prototype will misjudge the data to be in the same way of order, 0 < 1 < 2. But this isn’t the issue at all. To mitigate this difficulty, we employ one hot encoder.
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One Hot Encoder
One-Hot Encoding is another prominent protocol for dealing with categorical variables. It solely establishes the following characteristics established on the volume of distinct values in the categorical feature. Entire distinct values in the classification will be enlarged as an outline. One hot encoding takes a section which has categorical data, which has an existing label encoded and then divides the section into numerous sections. The volumes are rebuilt by 1s and 0s, counting on which section has what value.
The one-hot encoder does not approve 1-D arrays. The input should always be a 2-D array.
The data ratified to the encoder should not include strings.
Vastly of the prevailing machine learning algorithms cannot be committed to categorical data. Rather, the categorical data requires to be modified to numerical data. One-hot encoding is one of the strategies utilized to conduct this conversion. This technique is primarily utilized where deep learning methods are to correlate to sequential succession problems.
One-hot encoding is practically the manifestation of categorical variables as binary vectors. The categorical values are initially mapped out to integer values. Every integer value is exemplified as a binary vector that is all 0s.
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But what will happen if we have multiple files to handle?
Scikit-learn is susceptible to the arrangement of sections, so if the training dataset and test datasets get contradictions in it, the results will be an absurdity. This could transpire if a categorical had several numbers of values in the training data vs the test data.
Assure the test data is encoded in an identical way as the training data with the align command. The align command gives rise to security that the sections appear in the exact decree in both datasets.
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The globe is jammed with categorical data. An analyst will be a much more beneficial data scientist if you know how to use this data. Hence to anyone who seeks to work on such models must be well acquainted with the usage of label encoder and one hot encoder in machine learning.
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Which algorithms require the use of one hot encoding?
One hot encoding process is used to deal with categorical variables. This process converts the categorical variables to make it easier for machine learning algorithms to use the variables for better prediction. The algorithms that take only numerical values as inputs require only one hot encoding process to convert the categorical variables. Some of these machine learning algorithms are logistic regression, linear regression, support vector machine, etc. However, some algorithms, like Markov Chain, Naive Bayes, etc., do not require encoding because they are capable of dealing with joint discrete distributions.
When is it preferred to use one hot encoding in deep learning?
One Hot Encoding is a powerful data transformation and preprocessing approach that helps ML models comprehend the provided data. Basically, one hot encoding is used when the ML algorithm is incapable of working with categorical variables, thus, one hot encoding converts them into a suitable form. The use of one hot encoding is the most preferred when the features of the categorical variables to be converted are not ordinal. Also, one hot encoding works effectively when the number of categorical features present in the given dataset is very less.
What is meant by the term Dummy Variable Trap?
The Dummy variable trap is one of the problems faced by the one-hot encoding process. When a categorical dataset has strongly linked variables, this occurs. As a consequence, the outcome of one variable may be easily anticipated using the remaining variables when the one hot encoding procedure is used. As a result of the Dummy Variable Trap, another issue known as multicollinearity arises.