Tag：Logarithm
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Time：2020-10-31
By Michael GroganCompile | VKSource: toward Data Science Monte Carlo method has been widely used in finance and other fields to model various risk scenarios. However, this method also has important applications in other aspects of time series analysis. In this particular example, let’s look at how the Monte Carlo method can be used to […]
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Time：2020-10-27
1. Definition Exponential distribution family refers to a class of distribution functions with specific forms, which are as follows:$$p (Y | / ETA) = B (y) e ^ {ETA ^ TT (y) – A (/ ETA)} = \ dfrac {B (y) e ^ {ETA ^ TT (y)}} {e ^ {a (ETA)}} begin {cases} ETA: parameter […]
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Time：2020-10-6
By Mandy GuCompile | FlinSource: towards science Logistic regression is used to model the probability of event occurrence by estimating the logarithmic probability of event occurrence. If we assume that there is a linear relationship between logarithmic ratio and j independent variables, then we can model the probability p of event occurrence as follows: You […]
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Time：2020-3-21
Preface This paper uses tensorflow to train the logistic regression model, and compares it with scikit learn. Dataset from Andrew NG’s open online course deep learning Code #!/usr/bin/env python # -*- coding=utf-8 -*- #@ Author: Chen Zhiping # @date: 2017-01-04 # @description: compare the logistics regression of tensorflow with sklearn based on the exercise of […]
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Time：2020-2-28
We know that the perceptron algorithm can’t do anything for the data which can’t be completely linearly segmented. In this paper, we will introduce another very effective binary classification model – logical regression. It is widely used in classification tasks Logical regression is a classification model. Before implementation, we will introduce several concepts: Odds ratio: […]
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Time：2019-12-4
Logistic regression (classification) Sigmoid function and binomial logistic regression model \(sigmoid\)The function is: \[ sigmoid(x)=\pi(x)=\frac{1}{1+e^{-x}}\\ \]among\(x \in \mathbb{R}\)，\(sigmoid(x)\in (0,1)\). And its derivative \[ \pi'(x)=\pi(x)(1-\pi(x))\\ \] Binomial logistic regression model is the conditional probability distribution as follows: \[ P(Y=1|x)=\frac{exp(w \cdot x+b)}{1+exp(w \cdot x+b)}\\ P(Y=0|x)=\frac{1}{1+exp(w \cdot x+b)}\\ \]among\(x \in \mathbf{R}^{n}\)，\(Y \in\{0,1\}\)For output,\(w \in \mathbf{R}^{n}\)and\(b \in \mathbf{R}\)As parameters\(w\)Is […]
