Search results for "regression"

A Gentle Introduction to Maximum Likelihood Estimation for Linear Regression

A Gentle Introduction to Linear Regression With Maximum Likelihood Estimation

Linear regression is a classical model for predicting a numerical quantity. The parameters of a linear regression model can be estimated using a least squares procedure or by a maximum likelihood estimation procedure. Maximum likelihood estimation is a probabilistic framework for automatically finding the probability distribution and parameters that best describe the observed data. Supervised […]

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Zoomed in ACF and PACF plots for the univariate series of power consumption

Autoregression Forecast Model for Household Electricity Consumption

Given the rise of smart electricity meters and the wide adoption of electricity generation technology like solar panels, there is a wealth of electricity usage data available. This data represents a multivariate time series of power-related variables that in turn could be used to model and even forecast future electricity consumption. Autocorrelation models are very […]

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What is the difference between “classification” and “regression”?

A What is the difference between “classification” and “regression”? Classification involves assigning an observation a label. Some examples include: Assigning an email a label of “spam” or “not spam”. Assigning a credit card transaction a label of “fraud” or “ok”. Assigning a medical record a label of “healthy” or “sick”. Regression involves predicting a numerical […]

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Difference Between Classification and Regression in Machine Learning

Difference Between Classification and Regression in Machine Learning

There is an important difference between classification and regression problems. Fundamentally, classification is about predicting a label and regression is about predicting a quantity. I often see questions such as: How do I calculate accuracy for my regression problem? Questions like this are a symptom of not truly understanding the difference between classification and regression […]

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How To Implement Logistic Regression With Stochastic Gradient Descent From Scratch With Python

How To Implement Logistic Regression From Scratch in Python

Logistic regression is the go-to linear classification algorithm for two-class problems. It is easy to implement, easy to understand and gets great results on a wide variety of problems, even when the expectations the method has of your data are violated. In this tutorial, you will discover how to implement logistic regression with stochastic gradient […]

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How to Implement Linear Regression With Stochastic Gradient Descent From Scratch With Python

How to Implement Linear Regression From Scratch in Python

The core of many machine learning algorithms is optimization. Optimization algorithms are used by machine learning algorithms to find a good set of model parameters given a training dataset. The most common optimization algorithm used in machine learning is stochastic gradient descent. In this tutorial, you will discover how to implement stochastic gradient descent to […]

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