Why use normalization in machine learning

In machine learning, normalization is a common data preprocessing method. Its main purpose is to eliminate dimensional differences between features by scaling the data to the same range. Dimensional differences refer to the different value ranges and units of different features, which may have an impact on the performance and stability of the model. Through normalization, we can scale the value ranges of different features into the same interval, thereby eliminating the impact of dimensional differences. Doing so helps improve model performance and stability. Commonly used normalization methods include maximum and minimum value normalization and Z-score normalization. Min-Max Normalization scales the data to the range of [0, 1]. The specific method is to linearly transform the value of each feature so that the minimum value corresponds to 0 and the maximum value corresponds to 1. Z-score normalization (Standardization) transforms data into a standard normal distribution by subtracting the mean and dividing by the standard deviation. Doing this adjusts the mean of the data to 0 and the standard deviation to 1. Normalization processing is widely used in machine learning and can improve model performance and stability. In feature engineering, normalization can scale the value ranges of different features to the same interval, improving model performance and stability. In image processing, normalization can scale pixel values ​​to the range [0,1] to facilitate subsequent processing and analysis. In natural language processing, normalization converts text data into numerical vectors for easy processing and analysis by machine learning algorithms. The application of normalization can make the data have similar scales and prevent different features from biasing the model. Through normalization, data features can be better utilized, improving model performance and the reliability of results.

The purpose and significance of normalization processing

1. Reduce the dimensional difference of the data

There may be huge differences in the value ranges of different features, causing some features to The impact of model training results is greater. Through normalization, the range of eigenvalues ​​is scaled to the same interval to eliminate the influence of dimensional differences. This ensures that each feature's contribution to the model is relatively balanced and improves the stability and accuracy of training.

2. Improve the convergence speed of the model

For algorithms based on gradient descent, such as logistic regression and support vector machines, normalization processing has an important impact on the convergence speed and results of the model. Failure to perform normalization may result in slow convergence or local optimal solutions. Normalization can speed up the gradient descent algorithm to find the global optimal solution.

3. Enhance the stability and accuracy of the model

In some data sets, there is a strong correlation between features, which can lead to model overfitting. Through normalization, the correlation between features can be reduced and the stability and accuracy of the model can be improved.

4. Convenient model interpretation and visualization

The normalized data is easier to understand and visualize, which is helpful for model interpretation and visual display of results.

In short, normalization plays an important role in machine learning, which can improve the performance and stability of the model, and also facilitates the interpretation and visualization of data.

Commonly used normalization methods in machine learning

In machine learning, we usually use the following two normalization methods:

Minimum-maximum normalization: This This method is also called dispersion standardization. Its basic idea is to map the original data to the range of [0,1]. The formula is as follows:

x_{new}=\frac{x-x_{min }}{x_{max}-x_{min}}

Where, x is the original data, x_{min} and x_{max} are the minimum and maximum values ​​in the data set respectively.

Z-Score normalization: This method is also called standard deviation standardization. Its basic idea is to map the original data to a normal distribution with a mean of 0 and a standard deviation of 1. The formula is as follows:

x_{new}=\frac{x-\mu}{\sigma}

Where, x is the original data, \mu and \sigma are the mean and standard deviation in the data set respectively .

Both methods can effectively normalize data, eliminate dimensional differences between features, and improve the stability and accuracy of the model. In practical applications, we usually choose an appropriate normalization method based on the distribution of data and the requirements of the model.

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