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5 Types of Regressions for your Machine Learning Toolbox

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However, some seasoned techniques are here to stay. At the top of the list are regression techniques. As long as this number is as high, you will encounter regressions during your machine learning career. Even if you don't use them yourself, it is essential to be aware of the different flavors and which problems they tackle. In this post, I provide you with a quick overview of five different (groups of) regressions.


4 statistical processes that every data scientist should know

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The depth and variety of skills that fit under the analytics umbrella are extensive. Different roles -- such as strategic analysts, digital analysts, data scientists, data engineers -- require distinct skillsets and varying levels of technical expertise. However, a handful of statistical processes are so common that every analyst should be acquainted with them. Further, it's beneficial to know how to code these in at least one programming language (or if not, in Excel). Below, are 4 of the most common and versatile statistical methods used in business, along with examples and educational sources.


Clustering responses to define dependent variable for logistic regression

@machinelearnbot

Some colleagues of mine are working with survey responses, and are attempting to predict behaviors with demographic data. So, the plan is to define a dependent variable from some combination of responses to the survey questions, and then use a regression technique to model this dependent variable using other characteristics of the respondents. We all agree on the 5 or so questions that will define the dependent variable, but we disagree on how to specify the definition. I want to look at the actual questions being answered, and create a "score" as a weighted count of the'yeses' to the questions (weights based on how "on point" each question is to the behavior we are trying to define). My colleagues thought that this was too imprecise, and particularly criticised the'intuitive' weight assignment.


How the Logistic Regression Model Works in Machine Learning

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In this article, we are going to learn how the logistic regression model works in machine learning. The logistic regression model is one member of the supervised classification algorithm family. The building block concepts of logistic regression can be helpful in deep learning while building the neural networks. Logistic regression classifier is more like a linear classifier which uses the calculated logits (score) to predict the target class. If you are not familiar with the concepts of the logits, don't frighten.


Comprehensive Guide To Logistic Regression In R Edureka

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A logistic regression model is said to be statistically significant only when the p-Values are less than the pre-determined statistical significance level, which is ideally 0.05. The p-value for each coefficient is represented as a probability Pr( z). We see here that both the coefficients have a very low p-value which means that both the coefficients are essential in computing the response variable. The stars corresponding to the p-values indicate the significance of that respective variable. Since in our model, both the p values have a 3 star, this indicates that both the variables are extremely significant in predicting the response variable.