# Classification or Regression? Choosing the Right Model

Regression and classification are the two primary categories of supervised learning techniques. When the output labels are continuous values, such as when trying to forecast the price of a house based on its characteristics, regression algorithms are the type of algorithm that is utilised. Classification algorithms are employed in situations in which the output labels are discrete values. An example of this would be determining whether or not the content of an email is spam.

## Classification

A form of supervised learning known as classification is used for data in order to make educated guesses about the labels that will be assigned to each class. One way to think of it is as a subset of supervised learning, but with an emphasis on predictions rather than the data itself.

In classification, we develop a model using Machine Learning to assist us in distinguishing between distinct groups of data. Because of the model, we are able to investigate a variety of variables and how they are related to the response variable.

In order to build the model, you must first divide the data into two groups—the training set and the testing set—and then assign labels to each of those groups. This enables the machine to understand what information is significant and what information is not significant when it comes time to make predictions.

### Classification Algorithms:

There is a wide variety of classification algorithms, each of which has both advantages and disadvantages over the others. The following are some of the classification methods that are utilised most frequently:

Logistic Regression: Logistic regression is a statistical method that is used to model the probability of a binary or categorical outcome based on one or more predictor variables. This can be done by analysing the relationship between the outcome and the predictor variables.

Decision Tree: Decision Trees are a type of tree-based model that classifies inputs depending on the attributes of the items being analysed by making use of a sequence of binary decisions.

Random Forest: Random Forest is an ensemble model that is built on trees and mixes many decision trees in order to increase accuracy and decrease overfitting. Random Forest is an example of a tree-based ensemble model.

Support Vector Machine: Support Vector Machines (SVMs) are a form of model that distinguishes between classes by locating the hyperplane that creates the greatest amount of space between them.

K-Nearest Neighbours: K-Nearest Neighbours (KNN) is a straightforward technique that assigns classes to fresh inputs based on the classes of the K neighbours in the training data that are geographically closest to them.

Naive Bayes: Naive Bayes is a probabilistic model that determines the conditional probability of each class given the input attributes. This model is also known as the Naive Bayes algorithm.

Ensemble Learning: An ensemble learning method known as “Gradient Boosting” combines weak classifiers into a strong classifier by iteratively adding models that fix the faults of the models that came before them.

Artificial Neural Networks: Neural Networks are a form of model that may discover complicated patterns in the data since they consist of numerous layers of interconnected nodes.

## Regression

A process of developing a model for distinguishing data into continuous real values is what is meant when one refers to the concept of regression. The challenge of predicting how much something will cost based on its features is by far the most common one that the regression method is used to solve.

A strategy for analysing the relationship between two or more variables in order to evaluate the possibility of a correlation between the variables.

In a typical case of regression analysis, we will have one variable that is dependent on another variable that is independent. The dependent variable is the one whose behaviour we are attempting to forecast based on the independent variable.

If we want to determine whether or not a person’s height can be predicted based on their weight, for instance, the dependent variable, in this case, would be height, while the independent variable would be weight.

The following diagram explains the working principle of classification and regression:

### Regression Algorithms

The following are some of the regression algorithms that are utilised most frequently:

Linear regression: Linear regression is a statistical method that represents the relationship between a dependent variable and one or more independent variables as a linear equation. This can be done with one or more independent variables as well.

Polynomial Regression: Polynomial Regression is a type of linear regression that describes the relationship between the dependent variable and the independent factors as a polynomial equation. This is a version of the more common linear regression.

Ridge Regression: Ridge regression is a form of linear regression that differs from the traditional form in that it employs L2 regularisation to prevent overfitting. This is accomplished by including a penalty term in the loss function.

Lasso Regression: A variant of linear regression known as lasso regression, lassie regression makes use of L1 regularisation, which adds a penalty term to the loss function in order to encourage sparse solutions.

Elastic Net Regression: A combination of ridge and lasso regression, elastic net regression makes use of both L1 and L2 regularisation to strike a compromise between the complexity of the model and its sparsity.

Decision Trees: A tree-based model that predicts a continuous output by recursively partitioning the data based on the input attributes is referred to as a decision tree.

Random Forest: Random Forest is an ensemble model that is built on trees and mixes many decision trees in order to increase accuracy and decrease overfitting. Random Forest is an example of a tree-based ensemble model.

Support Vector Regression (SVR): Support Vector Regression (SVR) is a sort of regression that locates the hyperplane that maximises the margin between the predicted values and the actual values. This hyperplane is called the support vector regression solution.

Neural Networks: Neural Networks are a sort of model that can learn complicated non-linear relationships between the input data and the output values. These types of interactions can be learned by the model.