In solving data science problems, having the right approach is of critical importance and can often mean the difference between jumbling up and coming up with the right solution. In the beginning, data scientists often tend to confuse between the two – unable to figure out the small technical details that are important to attack the problem with the right approach.
Even with experienced and seasoned data scientists, the differences can easily confuse and this makes it challenging to apply the right approach. In this discourse, we will take a deeper dive into the differences and similarities with the two important data science algorithms – classification and regression.
Both these approaches should be essential tools in the arsenal of any data scientists in solving business problems. Hence, a crucial understanding is vital to select the right models, do the appropriate fine-tuning, and deploy the right solution that will give a lift to your business.
Regression vs Classification
Firstly, the important similarity – both regression and classification are categorized under supervised machine learning approaches. What is a supervised machine learning approach? It is a set of machine learning algorithms that train the model using real-world datasets ( called training datasets) to make predictions.
The data that is used to train the model needs to be well labelled and clean; the model will learn from the training data the relationship between the independent variables and the predictor variable. It is in contrast with the unsupervised machine learning approach, which asks the model to identify patterns within the data all by itself, thus find the mapping function by examining patterns inherent within the dataset.
A supervised machine learning approach tries to solve the mapping function, y = f(x), where x refers to the input variables, and y is the mapping function. By solving the mapping function, it can be quickly and conveniently transferred to the real-world dataset.
Both the classification and regression functions can do this, as well as any other supervised machine learning approach. But the significant difference and regression approaches are that while in a regression, the output variable ‘y’ is numeric and continuous (can be an integer or floating-point values), in the classification algorithm, the output variable ‘y’ is discrete and categorical.
So, if you are predicting variables such as salary, life expectancy, churn probability – then these variables will be numeric and continuous.
For example, suppose that a financial institution is interested in profiling its loan applicants in order to gauge the likelihood of their default. The data scientist can approach the problem in two major ways – it can either assign a probability ( which will be a range of continuous floating-point numbers between 0 and 1) to each loan applicant, or it simply gives a set of binary outputs- corresponding to PASS/ FAIL.
Both the approaches will take the same set of input variables – such as applicant credit history, salary information, demographic, age, macroeconomic conditions etc. But the difference between the two approaches is that while the former scores each applicant, which can be useful to make relativistic calculations, such as how much more likely is one individual against another.
The output can also be used for other analyses. However, in the latter case, the algorithm classifies the entire data set of individual profiles into either Yes or No, which can then be used to judge whether it is safe to give credit. Note that both the yes and no classes can have considerable variation within the sub-class.
But here with the classification approach, we are not interested in figuring out the variation within each sub-group. Classification can be used for other purposes, such as for classifying whether the incoming email is spam or not-spam.
On the other hand, weather prediction ( weather being able to take on a range of continuous values), will typically require a regression approach. If instead, we were only interested in predicting whether it would rain or not rain, then the same weather dataset might be more appropriately put into the classification system. Thus as we can see, the use case will determine which algorithm will be more suited to use.
Regression algorithms consist of linear regression, multivariate regression, support vector models and regression tree, among others. The classification approach utilizes decision trees, Naive Bayes, Logistics Regression, among others.
By understanding the difference between these approaches and algorithms, you will be better able to select and apply the right one to your business-specific use cases – thus helping you to arrive quickly at the right solution.
Classification and Regression Algorithm Types
Let us go deep and understand each of these algorithm types that are used in regression and classification.
Linear Regression – In linear regression, the relationship between two variables is estimated by plotting a straight, best-fit line. There are going to be other measurements needed to gauge the strength of the best-fit line plotted, such as the strength of fit, variance, standard deviation, r-squared value, among others. Learn more about regression models in Machine Learning.
Polynomial Regression – In polynomial regression models, relationships are measured between ‘several’ input variables, and the predictor or ‘output’ variable. Learn more about the regression models.
Decision Tree Algorithm – In the decision tree algorithm, the data set is classified with the help of a decision tree – where each node of the tree is a test case, and every branch that arises at each node of the tree corresponds to a possible value of the attribute.
Random Forest Algorithm – Random forest, as the name suggests, is built by adding up several decision tree algorithms. The model then aggregates the output from the different decision trees and comes up with the final prediction, which occurs by majority voting of the individual decision trees.
The final output given by the decision tree is more accurate than that provided by any of the individual decision trees. ‘Random Forests often tend to suffer from overfitting problems, but which can be fine-tuned with cross-validation and other methods
K nearest neighbour – K nearest neighbour is a robust classification algorithm which works on the principle that similar things remain in close proximity to each other. When the new variable is put into the prediction algorithm, then it tries to assign to a group based on its proximity to the datasets. Learn more about KNN.
As a data scientist, you need to have a fundamental and essential understanding of the different classification and regression approaches, the techniques involved will help you as a data scientist to apply the right set of tools, to come up with an appropriate solution that will benefit your business.
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