A data scientist is building a machine learning model. One of the features in the training data is location, which has three categories: Harare, Mutare, and Bulawayo. What is the best way for the data scientist to transform this column so that it can be used in a support vector machine algorithm?Label encodingStandard ScalingOne hot encodingOrdinal encodingMini Max Scaling
Question
A data scientist is building a machine learning model. One of the features in the training data is location, which has three categories: Harare, Mutare, and Bulawayo. What is the best way for the data scientist to transform this column so that it can be used in a support vector machine algorithm?Label encodingStandard ScalingOne hot encodingOrdinal encodingMini Max Scaling
Solution
The best way to transform the location column for use in a support vector machine algorithm would be through One Hot Encoding.
Here's why:
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One Hot Encoding is a process of converting categorical data variables so they can be provided to machine learning algorithms to improve predictions. With one hot, we convert each category value into a new column and assign a 1 or 0 (True/False) value to the column.
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This method is needed for the model because machine learning algorithms cannot work with categorical data directly. Categorical data must be converted to numbers.
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In this case, the location column contains three categories: Harare, Mutare, and Bulawayo. If we use One Hot Encoding, we will have three new columns, one for each city. If the location is Harare, it will be represented by [1, 0, 0], Mutare by [0, 1, 0], and Bulawayo by [0, 0, 1].
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This method is better than Label Encoding for this particular situation because Label Encoding might introduce a new problem since it uses numerical sequence. The algorithm might misinterpret the numerical sequence as ordinal, which is not the case here.
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Standard Scaling, Mini Max Scaling, and Ordinal Encoding are not suitable for this case. Standard and Mini Max Scaling are used for numerical data, not categorical. Ordinal Encoding is used when the categorical variable holds any significance in order, which is not the case here.
So, One Hot Encoding is the best method to transform the location column for use in a support vector machine algorithm.
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