Is feature normalization important for numerical data?
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🖥️📊🔢 Today's Q&A in Data Science!! 📚📖📝"Is feature normalization important for numerical data?"
1. What is Numerical Data?
Numerical data is divided into discrete numeric data that can be divided, such as dice scales and population counts, and continuous numeric data that cannot be divided, such as height and weight.
2. What is feature normalization?
Feature normalization is a method of scaling the range of data to a specific interval. Normalization is done to reduce the difficulty of calculations, for example, when the units are different, such as dollars, won, or yen.
Representative normalization techniques include min-max scaling using maximum and minimum values, z-normalization using the mean and variance of the data distribution, log normalization, and winsorizing, which excludes the top and bottom n% outliers from min-max scaling.
Numerical data is divided into discrete numeric data that can be divided, such as dice scales and population counts, and continuous numeric data that cannot be divided, such as height and weight.
2. What is feature normalization?
Feature normalization is a method of scaling the range of data to a specific interval. Normalization is done to reduce the difficulty of calculations, for example, when the units are different, such as dollars, won, or yen.
Representative normalization techniques include min-max scaling using maximum and minimum values, z-normalization using the mean and variance of the data distribution, log normalization, and winsorizing, which excludes the top and bottom n% outliers from min-max scaling.
3. Is feature normalization important for numerical data?
In conclusion, feature normalization is important for numerical data.
First of all, it is important in terms of computational difficulty. Since deep learning involves large-scale data computation, computing numerical data with large units increases the computational difficulty and can take a long time if normalization is not performed to achieve the same result.
Also, if the values of each feature are distributed over different ranges, it becomes difficult to make direct comparisons or calculations between features. For example, height and weight are both numeric features, but they have different units, making direct comparisons difficult. By using normalization to bring the features into the same range, comparisons and computations become possible, which is beneficial for model training.
First of all, it is important in terms of computational difficulty. Since deep learning involves large-scale data computation, computing numerical data with large units increases the computational difficulty and can take a long time if normalization is not performed to achieve the same result.
Also, if the values of each feature are distributed over different ranges, it becomes difficult to make direct comparisons or calculations between features. For example, height and weight are both numeric features, but they have different units, making direct comparisons difficult. By using normalization to bring the features into the same range, comparisons and computations become possible, which is beneficial for model training.
In addition, some machine learning algorithms put weight or calculate distances based on the range of input data. In these cases, normalized data can improve the performance of the model.
However, when performing feature normalization, methods such as min-max scaling and z-normalization can be strongly affected by outliers. Therefore, you should also consider how to handle outliers. For data that contains outliers, consider using statistical methods or outlier detection algorithms to remove them.
4. Additionally, is feature normalization important for categorical data?
Let's further understand the importance of feature normalization in categorical data. Categorical data has no sense of order or size, and it needs to be transformed into a numerical form to apply to a model.
The most commonly used transformation techniques are one-hot encoding and label encoding. One-hot encoding creates a new feature in binary form for each categorical value, so that each categorical value is represented by a 0 and a 1, and the model can understand the relationship between categories. Label encoding is a way to convert categorical values to numerical data by mapping them to integers. This conversion makes categorical data applicable to the model, and the normalization process does not apply to categorical data.
However, with categorical data, feature engineering is important, and the transformation technique can affect the performance of the model. For example, applying one-hot encoding can increase the dimensionality of the features depending on the number of categories, which can increase the complexity of the model. Therefore, for categorical data, it is important to preprocess the data by selecting appropriate feature engineering and modeling techniques.
The most commonly used transformation techniques are one-hot encoding and label encoding. One-hot encoding creates a new feature in binary form for each categorical value, so that each categorical value is represented by a 0 and a 1, and the model can understand the relationship between categories. Label encoding is a way to convert categorical values to numerical data by mapping them to integers. This conversion makes categorical data applicable to the model, and the normalization process does not apply to categorical data.
However, with categorical data, feature engineering is important, and the transformation technique can affect the performance of the model. For example, applying one-hot encoding can increase the dimensionality of the features depending on the number of categories, which can increase the complexity of the model. Therefore, for categorical data, it is important to preprocess the data by selecting appropriate feature engineering and modeling techniques.
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