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Logistic Regression
Example: flipping a coin one time. The probability of getting a head = 𝑝 ( ℎ𝑒𝑎𝑑 )=1/2 Introduction Logistic regression is a supervised learning technique , which is basically a probabilistic class...
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Logistic Regression is one of the fundamental models used in Machine Learning. It is a classification technique and is best suited for predicting the categorical response variable. While linear…
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Logistic Regression Introduction Comparison to linear regression Types of logistic regression Binary logistic regression Sigmoid activation Decision boundary Making predictions Cost function Gradient ...
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Logistic Regression is supervised machine learning algorithm, used for binary class or multi class classification(also called one-vs-all Classification). This is one of the famous classification…
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Logistic regression models the probabilities for classification problems with two possible outcomes. It’s an extension of the linear regression model for classification problems. The linear regression...
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Logistic regression is a classification algorithm used to assign observations to a discrete set of classes. Unlike linear regression which outputs continuous number values, logistic regression…
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Now, to predict whether a user will purchase the product or not, one needs to find out the relationship between Age and Estimated Salary. Here User ID and Gender are not important factors for finding…...
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In previous blogs we used the Linear Regression technique to create models. But Linear Regression technique is unbounded, it has infinite possibilities and can be used only when the response variable…...
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In a previous article I explored linear regression — the foundation of all other advanced models used in machine learning and data science. Linear regression models continuous dependent variables…
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This post will walk you through logistic regression from the very basics. In order to master machine learning it is imperative to have the basics very clear. It may seem exhausting at first but once…
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A Comprehensive blog on Logistic Regression with explanation consisting of Math behind it and implementing it from scratch through code, along with R^2 and p-value evaluations.
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Logistic Regression - Overview What are the odds that an event will happen? Answering yes/no questions. Often we have to resolve questions with binary or yes/no outcomes. For example: Does a patient h...
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