Logistic Regression (LR) - Revision history

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BPeat at 15:59, 12 July 2019

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@@ Line 17: / Line 17: @@
 [https://www.google.com/search?q=Logistic+Regression+machine+learning+ML ...Google search]
-* [[AI Solver]] ... [[Algorithms]] ... [[Algorithm Administration|Administration]] ... [[Model Search]] ... [[Discriminative vs. Generative]] ... [[Optimizer]] ... [[Train, Validate, and Test]]
+* [[AI Solver]] ... [[Algorithms]] ... [[Algorithm Administration|Administration]] ... [[Model Search]] ... [[Discriminative vs. Generative]] ... [[Train, Validate, and Test]]
 ** [[...predict categories]]
 * [[Regression]] Analysis

@@ Line 27: / Line 27: @@
 Logistic regression is used when you have a classification problem - yes/no, or a number between 1 and 10 representing an answer on a survey question.
-Logistic Regression is another technique borrowed by machine learning from the field of statistics. It is the go-to method for binary classification problems (problems with two class values). Logistic regression is like linear regression in that the goal is to find the values for the coefficients that weight each input variable. Unlike linear regression, the prediction for the output is transformed using a non-linear function called the logistic function. The logistic function looks like a big S and will transform any value into the range 0 to 1. This is useful because we can apply a rule to the output of the logistic function to snap values to 0 and 1 (e.g. IF less than 0.5 then output 1) and predict a class value. Because of the way that the model is learned, the predictions made by logistic regression can also be used as the probability of a given data instance belonging to class 0 or class 1. This can be useful on problems where you need to give more rationale for a prediction. Like linear regression, logistic regression does work better when you remove attributes that are unrelated to the output variable as well as attributes that are very similar (correlated) to each other. It’s a fast model to learn and effective on binary classification problems. [https://helloacm.com/a-short-introduction-logistic-regression-algorithm/ A Short Introduction – Logistic Regression Algorithm | EOF (The Ultimate Computing & Technology Blog)]
+Logistic Regression is another technique borrowed by machine learning from the field of statistics. It is the go-to method for binary classification problems (problems with two class values). Logistic regression is like linear regression in that the goal is to find the values for the coefficients that [[Activation Functions#Weights|weight]] each input variable. Unlike linear regression, the prediction for the output is transformed using a non-linear function called the logistic function. The logistic function looks like a big S and will transform any value into the range 0 to 1. This is useful because we can apply a rule to the output of the logistic function to snap values to 0 and 1 (e.g. IF less than 0.5 then output 1) and predict a class value. Because of the way that the model is learned, the predictions made by logistic regression can also be used as the probability of a given data instance belonging to class 0 or class 1. This can be useful on problems where you need to give more rationale for a prediction. Like linear regression, logistic regression does work better when you remove attributes that are unrelated to the output variable as well as attributes that are very similar (correlated) to each other. It’s a fast model to learn and effective on binary classification problems. [https://helloacm.com/a-short-introduction-logistic-regression-algorithm/ A Short Introduction – Logistic Regression Algorithm | EOF (The Ultimate Computing & Technology Blog)]
 https://helloacm.com/wp-content/uploads/2016/03/logistic-regression-example.jpg

@@ Line 17: / Line 17: @@
 [https://www.google.com/search?q=Logistic+Regression+machine+learning+ML ...Google search]
-* [[AI Solver]]
+* [[AI Solver]] ... [[Algorithms]] ... [[Algorithm Administration|Administration]] ... [[Model Search]] ... [[Discriminative vs. Generative]] ... [[Optimizer]] ... [[Train, Validate, and Test]]
 ** [[...predict categories]]
 * [[Regression]] Analysis
-* [[Math for Intelligence]] ... [[Finding Paul Revere]]
+* [[Math for Intelligence]] ... [[Finding Paul Revere]] ... [[Social Network Analysis (SNA)]] ... [[Dot Product]] ... [[Kernel Trick]]
 * [https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/multiclass-logistic-regression Multiclass Logistic Regression | Microsoft]
 * [[Linear Regression]]

@@ Line 2: / Line 2: @@
 |title=PRIMO.ai
 |titlemode=append
-|keywords=artificial, intelligence, machine, learning, models, algorithms, data, singularity, moonshot, Tensorflow, Google, Nvidia, Microsoft, Azure, Amazon, AWS
+|keywords=ChatGPT, artificial, intelligence, machine, learning, GPT-4, GPT-5, NLP, NLG, NLC, NLU, models, data, singularity, moonshot, Sentience, AGI, Emergence, Moonshot, Explainable, TensorFlow, Google, Nvidia, Microsoft, Azure, Amazon, AWS, Hugging Face, OpenAI, Tensorflow, OpenAI, Google, Nvidia, Microsoft, Azure, Amazon, AWS, Meta, LLM, metaverse, assistants, agents, digital twin, IoT, Transhumanism, Immersive Reality, Generative AI, Conversational AI, Perplexity, Bing, You, Bard, Ernie, prompt Engineering LangChain, Video/Image, Vision, End-to-End Speech, Synthesize Speech, Speech Recognition, Stanford, MIT |description=Helpful resources for your journey with artificial intelligence; videos, articles, techniques, courses, profiles, and tools
 |description=Helpful resources for your journey with artificial intelligence; videos, articles, techniques, courses, profiles, and tools
 }}
 [https://www.youtube.com/results?search_query=Logistic+Regression+artificial+intelligence YouTube search...]
@@ Line 10: / Line 19: @@
 * [[AI Solver]]
 ** [[...predict categories]]
 * [[Regression]] Analysis
 * [[Math for Intelligence]] ... [[Finding Paul Revere]]

@@ Line 12: / Line 12: @@
 * [[Capabilities]]
 * [[Regression]] Analysis
-* [[Math for Intelligence]]
+* [[Math for Intelligence]] ... [[Finding Paul Revere]]
 * [https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/multiclass-logistic-regression Multiclass Logistic Regression | Microsoft]
 * [[Linear Regression]]

@@ Line 5: / Line 5: @@
 |description=Helpful resources for your journey with artificial intelligence; videos, articles, techniques, courses, profiles, and tools
 }}
-[http://www.youtube.com/results?search_query=Logistic+Regression+artificial+intelligence YouTube search...]
+[https://www.youtube.com/results?search_query=Logistic+Regression+artificial+intelligence YouTube search...]
-[http://www.google.com/search?q=Logistic+Regression+machine+learning+ML ...Google search]
+[https://www.google.com/search?q=Logistic+Regression+machine+learning+ML ...Google search]
 * [[AI Solver]]
@@ Line 13: / Line 13: @@
 * [[Regression]] Analysis
 * [[Math for Intelligence]]
-* [http://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/multiclass-logistic-regression Multiclass Logistic Regression | Microsoft]
+* [https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/multiclass-logistic-regression Multiclass Logistic Regression | Microsoft]
 * [[Linear Regression]]
-* [http://www.analyticsvidhya.com/blog/2015/08/comprehensive-guide-regression/ 7 Types of Regression Techniques you should know! | Sunil Ray]
+* [https://www.analyticsvidhya.com/blog/2015/08/comprehensive-guide-regression/ 7 Types of Regression Techniques you should know! | Sunil Ray]
 Logistic regression is used when you have a classification problem - yes/no, or a number between 1 and 10 representing an answer on a survey question.
-Logistic Regression is another technique borrowed by machine learning from the field of statistics. It is the go-to method for binary classification problems (problems with two class values). Logistic regression is like linear regression in that the goal is to find the values for the coefficients that weight each input variable. Unlike linear regression, the prediction for the output is transformed using a non-linear function called the logistic function. The logistic function looks like a big S and will transform any value into the range 0 to 1. This is useful because we can apply a rule to the output of the logistic function to snap values to 0 and 1 (e.g. IF less than 0.5 then output 1) and predict a class value. Because of the way that the model is learned, the predictions made by logistic regression can also be used as the probability of a given data instance belonging to class 0 or class 1. This can be useful on problems where you need to give more rationale for a prediction. Like linear regression, logistic regression does work better when you remove attributes that are unrelated to the output variable as well as attributes that are very similar (correlated) to each other. It’s a fast model to learn and effective on binary classification problems. [http://helloacm.com/a-short-introduction-logistic-regression-algorithm/ A Short Introduction – Logistic Regression Algorithm | EOF (The Ultimate Computing & Technology Blog)]
+Logistic Regression is another technique borrowed by machine learning from the field of statistics. It is the go-to method for binary classification problems (problems with two class values). Logistic regression is like linear regression in that the goal is to find the values for the coefficients that weight each input variable. Unlike linear regression, the prediction for the output is transformed using a non-linear function called the logistic function. The logistic function looks like a big S and will transform any value into the range 0 to 1. This is useful because we can apply a rule to the output of the logistic function to snap values to 0 and 1 (e.g. IF less than 0.5 then output 1) and predict a class value. Because of the way that the model is learned, the predictions made by logistic regression can also be used as the probability of a given data instance belonging to class 0 or class 1. This can be useful on problems where you need to give more rationale for a prediction. Like linear regression, logistic regression does work better when you remove attributes that are unrelated to the output variable as well as attributes that are very similar (correlated) to each other. It’s a fast model to learn and effective on binary classification problems. [https://helloacm.com/a-short-introduction-logistic-regression-algorithm/ A Short Introduction – Logistic Regression Algorithm | EOF (The Ultimate Computing & Technology Blog)]
-http://helloacm.com/wp-content/uploads/2016/03/logistic-regression-example.jpg
+https://helloacm.com/wp-content/uploads/2016/03/logistic-regression-example.jpg
-Logistic Regression is used where a discreet output is expected such as the occurrence of some event (Ex. predict whether rain will occur or not). Usually, Logistic regression uses some function to squeeze values to a particular range. “Sigmoid” (Logistic function) is one of such function which has “S” shape curve used for binary classification. It converts values to the range of 0, 1 which interpreted as a probability of occurring some event.  y = e^(b0 + b1*x) / (1 + e^(b0 + b1*x))  this is a simple logistic regression equation where b0, b1 are constants. While training values for these will be calculated such that the error between prediction and actual value become minimum. [http://towardsdatascience.com/10-machine-learning-algorithms-you-need-to-know-77fb0055fe0 10 Machine Learning Algorithms You need to Know | Sidath Asir @ Medium]
+Logistic Regression is used where a discreet output is expected such as the occurrence of some event (Ex. predict whether rain will occur or not). Usually, Logistic regression uses some function to squeeze values to a particular range. “Sigmoid” (Logistic function) is one of such function which has “S” shape curve used for binary classification. It converts values to the range of 0, 1 which interpreted as a probability of occurring some event.  y = e^(b0 + b1*x) / (1 + e^(b0 + b1*x))  this is a simple logistic regression equation where b0, b1 are constants. While training values for these will be calculated such that the error between prediction and actual value become minimum. [https://towardsdatascience.com/10-machine-learning-algorithms-you-need-to-know-77fb0055fe0 10 Machine Learning Algorithms You need to Know | Sidath Asir @ Medium]
 <youtube>H6ii7NFdDeg</youtube>

← Older revision		Revision as of 16:17, 26 April 2020
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	* [http://www.analyticsvidhya.com/blog/2015/08/comprehensive-guide-regression/ 7 Types of Regression Techniques you should know! \| Sunil Ray]		* [http://www.analyticsvidhya.com/blog/2015/08/comprehensive-guide-regression/ 7 Types of Regression Techniques you should know! \| Sunil Ray]

		+	Logistic regression is used when you have a classification problem - yes/no, or a number between 1 and 10 representing an answer on a survey question.

	Logistic Regression is another technique borrowed by machine learning from the field of statistics. It is the go-to method for binary classification problems (problems with two class values). Logistic regression is like linear regression in that the goal is to find the values for the coefficients that weight each input variable. Unlike linear regression, the prediction for the output is transformed using a non-linear function called the logistic function. The logistic function looks like a big S and will transform any value into the range 0 to 1. This is useful because we can apply a rule to the output of the logistic function to snap values to 0 and 1 (e.g. IF less than 0.5 then output 1) and predict a class value. Because of the way that the model is learned, the predictions made by logistic regression can also be used as the probability of a given data instance belonging to class 0 or class 1. This can be useful on problems where you need to give more rationale for a prediction. Like linear regression, logistic regression does work better when you remove attributes that are unrelated to the output variable as well as attributes that are very similar (correlated) to each other. It’s a fast model to learn and effective on binary classification problems. [http://helloacm.com/a-short-introduction-logistic-regression-algorithm/ A Short Introduction – Logistic Regression Algorithm \| EOF (The Ultimate Computing & Technology Blog)]		Logistic Regression is another technique borrowed by machine learning from the field of statistics. It is the go-to method for binary classification problems (problems with two class values). Logistic regression is like linear regression in that the goal is to find the values for the coefficients that weight each input variable. Unlike linear regression, the prediction for the output is transformed using a non-linear function called the logistic function. The logistic function looks like a big S and will transform any value into the range 0 to 1. This is useful because we can apply a rule to the output of the logistic function to snap values to 0 and 1 (e.g. IF less than 0.5 then output 1) and predict a class value. Because of the way that the model is learned, the predictions made by logistic regression can also be used as the probability of a given data instance belonging to class 0 or class 1. This can be useful on problems where you need to give more rationale for a prediction. Like linear regression, logistic regression does work better when you remove attributes that are unrelated to the output variable as well as attributes that are very similar (correlated) to each other. It’s a fast model to learn and effective on binary classification problems. [http://helloacm.com/a-short-introduction-logistic-regression-algorithm/ A Short Introduction – Logistic Regression Algorithm \| EOF (The Ultimate Computing & Technology Blog)]