AI artificial intelligence preprocessing data
In the field of artificial intelligence (AI), data preprocessing is a very important part. It is the process of manipulating and cleaning the data before feeding it into the model. Data preprocessing can improve model accuracy, reliability, and interpretability.
This article will introduce in detail the methods and techniques of AI artificial intelligence preprocessing data.
data cleaning
Data cleaning is the first step in data preprocessing. It refers to the removal of unnecessary data such as noise, duplication, and missing values in the data set to ensure the quality and accuracy of the data.
Data cleaning can be done in several ways:
-
De-duplicate data : In the data set, sometimes there will be duplicate data, which will affect the training and prediction of the model. Therefore, we need to remove these duplicate data.
-
Removing Outliers : Outliers are values in a dataset that are significantly different from the rest of the data. These outliers may be due to data recording errors, measurement errors, or other reasons. Outliers can affect the performance of the model and thus need to be removed.
-
Filling Missing Values : In datasets, there are sometimes missing values. These missing values may be due to measurement errors, data entry errors, or other reasons. In order to ensure the completeness and accuracy of the data, we need to fill in the missing values.
data conversion
Data transformation refers to converting raw data into a form more suitable for machine learning algorithms.
Data conversion can be done in several ways:
-
Eigen scaling : Eigen scaling refers to scaling down or up the eigenvalues so that they are of the same order of magnitude. This can reduce the variance between eigenvalues and improve the performance of the model.
-
Feature Encoding : Feature encoding is the process of converting categorical features into numerical features. This enables categorical features to be processed by machine learning algorithms.
-
Feature Selection : Feature selection is the selection of the most relevant features from all available features. This can reduce the number of features and improve the performance of the model.
data normalization
Data normalization is the scaling of data to a specific range so that they can be processed by machine learning algorithms.
Data normalization can be done in several ways:
-
Min-Max Normalization : Min-Max normalization is scaling the data to a range between 0 and 1. This preserves the relative size relationship of the data.
-
Z-score normalization : Z-score normalization is to scale the data to a range with a mean of 0 and a standard deviation of 1. This can make data distributions more normalized so that they can be processed by machine learning algorithms.
Dataset partition
Data set partitioning is the process of dividing the original data set into training set, validation set and test set. This is to evaluate the performance and accuracy of machine learning models.
Dataset partitioning can be done in the following ways:
-
Random sampling : Random sampling is to randomly select a part of the data from the original data set as the training set, verification set and test set.
-
Stratified sampling : Stratified sampling is to select a certain proportion of data in the original data set and stratify it according to its characteristics to ensure that the data in the training set, validation set, and test set have similar feature distributions.
Summarize
This article introduces the methods and technologies of AI artificial intelligence preprocessing data, including data cleaning, data conversion, data normalization, and data set division. Data preprocessing is a very important part of machine learning, which can improve the accuracy, reliability and interpretability of the model. Choosing appropriate data preprocessing methods and techniques can improve the performance of machine learning models and make them more suitable for practical problems.