邏輯回歸數據_數據科學中的邏輯回歸

邏輯回歸數據

邏輯回歸 (Logistic Regression)

Logistic regression is an applied mathematics analysis methodology accustomed to predict a data price supported previous observations of a data set. Logistic regression has become a very important tool within the discipline of machine learning. The approach permits associate degree formula being employed in a very machine learning application to classify incoming data supported historical data. As additional relevant data comes in, the formula ought to get well at predicting classifications inside data sets.
Logistic regression can even play a job in data preparation activities by permitting data sets to be placed into specifically predefined buckets throughout the extract, transform, load (ETL) method to stage the data for analysis.

邏輯回歸是一種應用數學分析方法，習慣于預測支持數據集先前觀察的數據價格。邏輯回歸已成為機器學習領域中非常重要的工具。該方法允許在非常機器學習的應用程序中使用副學歷公式來對輸入數據支持的歷史數據進行分類。隨著更多相關數據的涌入，該公式應能很好地預測數據集中的分類。
通過允許將數據集放置在整個提取，轉換，加載(ETL)方法中的特定預定義存儲桶中，以進行數據分析，邏輯回歸甚至可以在數據準備活動中發揮作用。

A logistics regression model predicts a dependent data variable by analyzing the link between one or additional existing freelance variables. For instance, logistic regression may be accustomed to predict whether or not a political candidate can win or lose an associate degree election or whether a high school student is admitted to a specific faculty.

物流回歸模型通過分析一個或其他現有自由職業變量之間的聯系來預測因變量。例如，邏輯回歸可以習慣于預測政治候選人是否可以贏得或失去副學士學位選舉，或者高中生是否被錄取到特定的系。

The ensuing analytical model will take into thought multiple input criteria. Within the case of faculty acceptance, the model may contemplate factors like the student’s grade average, Sabbatum score and variety of extracurricular activities. Supported historical data concerning earlier outcomes involving equivalent input criteria, it then scores new cases on their chance of falling into a specific outcome class.

隨后的分析模型將考慮多種輸入標準。在教師接受的情況下，該模型可以考慮諸如學生的平均成績，Sabbatum分數和各種課外活動之類的因素。支持的歷史數據涉及涉及等效輸入標準的早期結果，然后根據新案例落入特定結果類別的機會對新案例進行評分。

物流回歸的目的和樣本 (Purpose and samples of logistics regression)

Logistic regression is one among the foremost unremarkably used machine learning algorithms for binary classification issues, that are issues with 2 category values, as well as predictions like "this or that", "yes or no" and "A or B".

Logistic回歸是用于二進制分類問題的最重要的機器學習算法之一，它是具有2個類別值的問題，以及諸如“ this or that” ， “ yes or no”和“ A或B”之類的預測。

The purpose of logistics regression is to estimate the possibilities of events, as well as crucial a relationship between options and therefore the chances of specific outcomes.
On examination of this is often predicting if a student can pass or fail associate degree exam once the quantity of hours spent finding out is provided as a feature and therefore the variables for the response have 2 values: pass and fail.

后勤回歸的目的是估計事件的可能性，以及期權之間的關系，以及由此得出的特定結果的可能性。
在檢查時，通常會預測學生是否可以通過一次發現花費的小時數作為一項功能來通過或通過副學士學位考試，因此答案的變量具有兩個值：通過和失敗。

Organizations will use insights from logistic regression outputs to reinforce their business ways so that they can do their business goals, as well as reducing expenses or losses and increasing ROI in promoting campaigns, for instance.

組織將利用邏輯回歸輸出的見解來加強其業務方式，以便他們能夠實現其業務目標，并減少開支或損失并提高促銷活動中的投資回報率。

An e-commerce company that mails expensive promotional offers to clients would like to understand whether or not a specific customer is probably going to retort to the offers or not. For instance, they'll wish to understand whether or not that client is a "responder" or a "non-answerer." In promoting, this is often referred to as propensity to answer modeling.

一家向客戶郵寄昂貴促銷優惠??的電子商務公司希望了解特定客戶是否可能會拒絕這些優惠。例如，他們希望了解該客戶是“響應者”還是“非回答者”。在推廣中，這通常被稱為回答建模的傾向。

Likewise, a Mastercard company develops a model to decide whether or not to issue a credit card to a client or not an attempt to predict whether the customer goes to default or not on the credit card supported such characteristics as annual financial gain, monthly Mastercard payments, and variety of defaults. In banking idiom, this is often called default propensity modeling.

同樣，萬事達卡公司開發一種模型，以決定是否向客戶發行信用卡，以嘗試預測客戶是否因支持的信用卡而違約，例如年度財務收益，每月萬事達卡付款以及各種默認值。在銀行習慣用法中，這通常稱為默認傾向建模。

物流回歸的用途 (Uses of Logistics Regression)

Logistic regression has become significantly widespread in on-line advertising, facultative marketers to predict the chance of specific web site users UN agency can click on specific advertisements as an affirmative or no proportion.

邏輯回歸在在線廣告中已變得非常普遍，兼職營銷人員可以預測特定網站用戶的機會。聯合國機構可以按肯定比例或不按比例點擊特定廣告。

Logistic regression can even be used in:

邏輯回歸甚至可以用于：

Healthcare to spot risk factors for diseases and set up preventive measures.
醫療保健要發現疾病的危險因素并制定預防措施。
Weather forecasting apps to predict downfall and climate.
天氣預報應用程序可預測降雨和氣候。
Voting apps to see if voters can vote for a specific candidate.
投票應用程序可查看選民是否可以為特定候選人投票。
Insurance to predict the probabilities that a policyholder can die before the term of the policy expires supported bound criteria, like gender, age, and physical examination.
保險以預測保單持有人在保單期限屆滿之前可能死亡的可能性，這是受支持的受約束標準，例如性別，年齡和身體檢查。
Banking to predict the probabilities that a loan mortal can default a loan or not, supported annual financial gain, past defaults, and past debts.
銀行業務預測貸款人可以違約或不違約的概率，支持的年度財務收益，過去的違約和過去的債務。

Logistic回歸與統計回歸 (Logistic Regression vs. Statistical Regression)

The main distinction between logistics regression and statistical regression is that logistic regression provides a relentless output, whereas statistical regression provides never-ending output.

后勤回歸和統計回歸之間的主要區別是邏輯回歸提供了無休止的輸出，而統計回歸提供了無休止的輸出。

In logistics regression, the end result, like a variable, solely features a restricted variety of attainable values. However, in statistical regression, the end result is continuous, which implies that it will have anybody of an infinite variety of attainable values.

在物流回歸中 ，最終結果像變量一樣，僅具有有限的各種可獲得值。但是，在統計回歸中，最終結果是連續的，這意味著它將擁有無限多種可實現值的任何人。

Logistic regression is employed once the response variable is categorical, like yes/no, true/false and pass/fail. statistical regression is employed once the response variable is continuous, like a variety of hours, height and weight.

一旦響應變量是分類的(例如是/否，是/否和通過/失敗)，就采用邏輯回歸 。一旦響應變量是連續的(如各種小時，身高和體重)，就采用統計回歸。

For example, given data on the time a student spent finding out which student's examination scores, logistics regression, and statistical regression will predict various things.

例如，給定有關學生花費時間的數據，以找出哪些學生的考試成績，后勤回歸和統計回歸將預測各種情況。

With logistics regression predictions, solely specific values or classes are allowed. Therefore, logistics regression will predict whether or not the coed passed or failing. Since statistical regression predictions are continuous, like numbers vary, it will predict the student's take a look at the score on a scale of 0-100.

對于物流回歸預測，僅允許使用特定值或類別。因此，物流回歸將預測男女學生是否通過或失敗。由于統計回歸預測是連續的，就像數字變化一樣，它將預測學生以0-100的比例查看分數。