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Data analysis method based on quantile logistic regression

A quantile regression and logistic regression technology, applied in the field of data analysis, can solve problems such as poor model prediction accuracy, high data requirements, and inability to provide accurate predictions of different quantiles d, and achieve the effect of improving robustness

Pending Publication Date: 2019-12-17
深圳索信达数据技术有限公司
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AI Technical Summary

Problems solved by technology

As a mean model, the logistic regression model cannot provide accurate predictions for different quantiles d
At the same time, the mean value model has high requirements for data and needs to detect and deal with outliers, otherwise the prediction accuracy of the model is very poor

Method used

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  • Data analysis method based on quantile logistic regression

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Embodiment Construction

[0036] The technical solution of the present invention will be described in detail below in conjunction with the accompanying drawings and preferred specific embodiments.

[0037] Such as figure 1 Shown, a kind of data analysis method based on quantile logistic regression of the present invention specifically includes with

[0038] Next steps:

[0039] A data analysis method based on quantile logistic regression, characterized in that: specifically comprises the following steps:

[0040] The first step is data cleaning and preprocessing; check the consistency of the original data, standardize the data format, remove duplicate data, abnormal data and invalid data, correct wrong data, fill in missing values ​​according to the situation, and convert categorical variables into numerical types variable.

[0041] For example: the data structure after data cleaning and preprocessing is: the response variable y is a binary classification variable, with a value of 1 or 0, representi...

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Abstract

The invention discloses a data analysis method based on quantile logistic regression, and relates to a data analysis method based on quantile logistic regression. When a traditional logistic regression model is used as a classifier, the given information is very limited, and the prediction accuracy of the model is very poor. The method comprises the steps of 1, data cleaning and preprocessing; 2,quantile logistic regression modeling is carried out; 3, parameters of the quantile logistic regression model is solved, and the parameters are solved to minimize the sum of weighted error absolute values; and 4 an unconstrained quantile regression objective function and a constrained quantile regression objective function are calculated. Researchers can research different groups more carefully, and more information can be obtained through data. Meanwhile, the quantile regression model is not sensitive to abnormal values, the prediction effect of the model is not affected by a small number ofabnormal values, and the robustness of the model is greatly improved.

Description

technical field [0001] The invention relates to the field of data analysis, in particular to a data analysis method based on quantile logistic regression. Background technique [0002] Logistic regression is often used for binary classification problems in data analysis. For the relationship between the response variable y=1 or 0 (corresponding to YES orNO) and k explanatory variables x, the following model is used: or logit(π)=β 0 +β 1 x 1 +L+β k x k , [0003] where π(x)=P(y=1|X=x) is the probability of y=1(YES). For example, in the case of credit card fraud, to perform logistic regression modeling on the relationship between judging customer fraudulent behavior (y=1 is yes, y=0 is no) and its characteristic variable X (income, occupation, application address, etc.), then in the When predicting, the model output π(x)=P(y=1|X=x) is the probability of a customer being fraudulent. If we set 0.5 as the limit, then P>0.5 means there is fraud, and P<0.5 means ther...

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Application Information

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IPC IPC(8): G06K9/62G06Q40/02
CPCG06Q40/03G06F18/24G06F18/10
Inventor 张舵
Owner 深圳索信达数据技术有限公司
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