# Classification of a response variable

## What is the response variable in the data set?

Response Variable is

**the result of the experiment where the explanatory variable is manipulated**. It is a factor whose variation is explained by the other factors. Response Variable is often referred to as the Dependent Variable or the Outcome Variable.## What is classification and regression?

**Classification is the task of predicting a discrete class label.**

**Regression is the task of predicting a continuous quantity**.

## What is a primary response variable?

Primary and Secondary Response Variables

Response variables are defined as **outcomes that will be used as the main evidence of the treatment effect of the investigational drug**. Treatment effect is defined as an effect that is expected to result from a therapy.

## When should you use classification over regression?

The most significant difference between regression vs classification is that while regression helps predict a continuous quantity,

**classification predicts discrete class labels**. There are also some overlaps between the two types of machine learning algorithms.## Can I use regression for classification?

As we all know, when we want to predict a continuous dependent variable from a number of independent variables, we used linear/polynomial regression. But when it comes to classification,

**we can’t use that anymore**. Fundamentally, classification is about predicting a label and regression is about predicting a quantity.## How do you convert regression to classification?

In order to transform a regression problem into a classification task

**two possible discretizations of a continuous output (target) vector y are presented and compared**. A very strict double (nested) cross-validation technique has been used for measuring performances of regression and multiclass classification SVMs.## Why linear regression is not suitable for classification?

There are two things that explain why Linear Regression is not suitable for classification. The first one is that

**Linear Regression deals with continuous values whereas classification problems mandate discrete values**. The second problem is regarding the shift in threshold value when new data points are added.## What is regression differentiate between classification and regression?

The Difference Between Regression vs. Classification

Regression Algorithms | Classification Algorithms |
---|---|

It attempt to find the best fit line, which predicts the output more accurately. | Classification tries to find the decision boundary, which divides the dataset into different classes. |

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## How is clustering different from classification?

The process of classifying the input instances based on their corresponding class labels is known as classification whereas

**grouping the instances based on their similarity without the help of class labels**is known as clustering.## Can classification problems be solved using linear regression?

Linear regression is a great algorithm but it is highly impacted by outliers. Hence

**we cannot use it to solve a classification problem**. We need an algorithm that absorbs the effects of outliers without impacting the final output. Logistic regression does that by using something called a Sigmoid function.## What is the main difference between classification regression and clustering techniques?

Regression and Classification are types of supervised learning algorithms while

**Clustering is a type of unsupervised algorithm**. When the output variable is continuous, then it is a regression problem whereas when it contains discrete values, it is a classification problem.## What are classification models used for?

Classification model: A classification model

**tries to draw some conclusion from the input values given for training**. It will predict the class labels/categories for the new data.## What is the key difference between classification and regression?

The key distinction between Classification vs Regression algorithms is Regression algorithms are used to determine continuous values such as price, income, age, etc. and Classification algorithms are used to forecast or classify the distinct values such as Real or False, Male or Female, Spam or Not Spam, etc.

## What do we mean by regression?

A regression is

**a statistical technique that relates a dependent variable to one or more independent (explanatory) variables**. A regression model is able to show whether changes observed in the dependent variable are associated with changes in one or more of the explanatory variables.## What is classification and regression algorithm machine learning?

The task of the classification algorithm is to map the input value(x) with the discrete output variable(y).

**Regression Algorithms are used with continuous data.****Classification Algorithms are used with discrete data**. In Regression, we try to find the best fit line, which can predict the output more accurately.## What is a classification in machine learning?

In machine learning, classification refers to

**a predictive modeling problem where a class label is predicted for a given example of input data**. Examples of classification problems include: Given an example, classify if it is spam or not. Given a handwritten character, classify it as one of the known characters.## Is regression same as correlation?

What is the difference between correlation and regression? The difference between these two statistical measurements is that

**correlation measures the degree of a relationship between two variables (x and y), whereas regression is how one variable affects another**.## What are the difference between correlation and regression?

**Correlation quantifies the strength of the linear relationship between a pair of variables, whereas regression expresses the relationship in the form of an equation**.

## Why is it called regression?

“Regression”

**comes from “regress” which in turn comes from latin “regressus” – to go back (to something)**. In that sense, regression is the technique that allows “to go back” from messy, hard to interpret data, to a clearer and more meaningful model.## What are 3 examples of correlation?

**Positive Correlation Examples**

- Example 1: Height vs. Weight.
- Example 2: Temperature vs. Ice Cream Sales.
- Example 1: Coffee Consumption vs. Intelligence.
- Example 2: Shoe Size vs. Movies Watched.

## What is another name for a regression line?

Another name for the regression line is the

**least squares line**because it is chosen so that the sum of the squares of the differences between the observedâ€‹ y-value and the value predicted by the line is as small as possible.## What are the types of correlation?

**Types of Correlation**

- Positive Linear Correlation. There is a positive linear correlation when the variable on the x -axis increases as the variable on the y -axis increases. …
- Negative Linear Correlation. …
- Non-linear Correlation (known as curvilinear correlation) …
- No Correlation.

## What are the 5 types of correlation?

**Different Types of Correlation**

- Positive and negative correlation.
- Linear and non-linear correlation.
- Simple, multiple, and partial correlation.