# Classification vs Regression in Machine Learning

We use classification and regression algorithms for supervised learning tasks in machine learning. We use both these algorithms to predict a class or a target value for a new data point if we are given labeled training data. This article discusses classification vs regression including their objective functions to analyze how similar or different these algorithms are.

## Classification in Machine Learning

Classification in machine learning is used to assign labels to new data points based on existing training data. While training a classification model, we use the training data to find a function to assign class labels to data points based on their features. The class labels are discrete in nature.

For example, we can use a classification model to identify if a person is obese or not. For this, we can use their height, weight, blood pressure, heart rate, body fat percentage, etc. Here, height, weight, body fat percentage, and other attributes become the independent features of the classification model. The dependent or target feature can be the category OBESE and NOT OBESE.

We first use a labeled dataset of people who have already identified as obese or not obese to train the classification model using an algorithm like K-Nearset neighbors classification. Once the model gets trained, we can use the features of unseen people to identify if they are obese or not. Here, the classification model will assign the OBESE or NOT OBESE based on the attributes of the new person.

Apart from K-Nearset Neighbors classification, there are also other classification algorithms like support vector machines, Naive Bayes, Decision trees, and random forest classifiers.

## Regression in Machine Learning

The working of Regression algorithms is almost similar to classification. The only difference is that the target values in a regression model are continuous values instead of discrete values or a label. While training a regression model, we use the training data to find a function that can calculate the value for the target variable of new data points based on their features. Here, the target variable is continuous in nature.

For example, we can use a regression model to identify the fat percentage of a person based on their height, weight, blood pressure, heart rate, etc. Here, height, weight, blood pressure, heart rate, and other attributes become the independent features for the regression algorithm. The fat percentage will be the target variable.

To implement regression, we will first use a dataset of people having all the independent attributes as well as their fat percentage. We will train the regression model using an algorithm like multiple regression or KNN regression. Once the model gets trained, we can use the features of unseen people to identify their fat percentage.

Some of the popular algorithms for regression in machine learning are linear regression, support vector regression, polynomial regression, etc.

## When to Use Classification vs Regression

Classification and regression both are supervised machine learning techniques. Hence, you can only use these algorithms if you have labeled training data with defined target labels or values.

• You should use classification for those machine-learning tasks in which you have to predict discrete class labels for a given data point.
• You should use regression for those machine learning tasks in which the target value is continuous in nature.

## Classification vs Regression Objective Functions

Classification and regression models are evaluated using very different objective functions although their basic idea of working is the same. As the nature of the target variable is different for both algorithms, the objective functions are also different. Let us have a discussion on classification vs regression objective function to know the objective functions used in classification as well as regression.

### Objective Functions For Classification Algorithms

In classification, we use an objective function to measure how well a model performs at predicting the correct class labels for a given set of inputs. Following are some of the objectives used for classification tasks.

1. Cross-entropy loss: We use cross-entropy loss to measure the difference between the predicted class probabilities and the true class probabilities. It aims to minimize the average negative log-likelihood of the correct class.
2. Hinge loss: We use hinge loss for linear classifiers such as support vector machines (SVMs). Hinge loss aims to maximize the margin between the decision boundary and the training examples and penalizes examples that are misclassified or lie too close to the boundary.
3. Logistic loss: Similar to cross-entropy loss, we use the logistic loss to measure the difference between the predicted class probabilities and the true class probabilities. As the name suggests, it is commonly used in logistic regression and aims to maximize the likelihood of the correct class labels.
4. Accuracy: While accuracy is not a traditional objective function, we often use it as a performance metric for classification tasks. Accuracy measures the proportion of correct predictions made by the model and can be useful for evaluating the overall performance of the model.
5. F1 score: The F1 score is another commonly used performance metric for classification tasks, particularly when dealing with imbalanced datasets. It balances the precision and recall of the model and is calculated as the harmonic mean of these two metrics.
6. AUC-ROC: The area under the receiver operating characteristic (ROC) curve is a popular performance metric for binary classification tasks. It measures the trade-off between the true positive rate and the false positive rate and provides an overall measure of the model’s ability to distinguish between positive and negative examples.

### Objective Functions For Regression Algorithms

In regression, the objective function is used to measure the quality of the model’s predictions. The most common objective functions used in regression are discussed below.

1. Mean Squared Error (MSE): Mean squared error is calculated as the average of the squared differences between the predicted values and the true values. We need to minimize the mean squared error for a regression model to get better outputs.
2. Mean Absolute Error (MAE): The mean absolute error is calculated as the average of the absolute differences between the predicted values and the true values. Again, we need to minimize the mean absolute error for the machine learning model to perform better.
3. Root Mean Squared Error (RMSE): The root mean squared error is the square root of the mean squared error. We use the root mean squared error to measure the standard deviation of the difference between the predicted and true values. We need to minimize root mean squared error for the model to perform better.
4. R-squared (R²): R-squared is a statistical measure that we use to measure the proportion of the variance in the dependent variable that is explained by the independent variables in the model. We need to maximize the R-squared value for the regression model to perform better.
5. Huber Loss: Huber loss is a hybrid between MSE and MAE. It is less sensitive to outliers than MSE and more sensitive than MAE. Again, the objective of a regression model is to minimize the Huber loss.
6. Quantile Loss: Quantile loss is used when the objective is to predict a certain quantile of the target variable. We need to minimize the quantile loss for better results in the regression model.

## Conclusion

In this article, we discussed classification vs regression to identify the differences in the working of these two algorithms. We also discussed the different objective functions for classification vs regression. To read about more machine learning concepts, you can read this article clustering vs classification in machine learning. You might also like this article on KNN vs K-Means.

Happy Learning!