CLASSIFICATION OF MACHINE LEARNING MODELS
Any Decision that we made as a buyer or as a business person it belongs to either classification problem or regression problem.
Updated
On this page
Updated November 2026. This was first published on 11 June 2020. The idea is the same. Any number below is what the original piece said then. It is not a new count.
Key takeaways
- Any Decision that we made as a buyer or as a business person it belongs to either classification problem or regression problem.
- A classification problem means when you have to decide as binary (Yes/No) or multi-class (Agree/Neutral/Disagree).
- A regression problem means when you have to set price for your product/service or setting discount percentage for any offer.
Any Decision that we made as a buyer or as a business person it belongs to either classification problem or regression problem. A classification problem means when you have to decide as binary (Yes/No) or multi-class (Agree/Neutral/Disagree). A regression problem means when you have to set price for your product/service or setting discount percentage for any offer. Here I will be discussing on Classification problem and different methods used to predict the Classification problem decision.
An example of a classification problem is a buyer is going to buy or not going to buy a certain product or service. a business person is always worried whether sales will happen or not, a doctor has to make a decision based on diagnosis result before starting any treatment. These are a few examples of classification problems. Here I am going to discuss various machine learning methods (algorithms) which can help you to make a more accurate decision and avoid loss.
Machine Learning Methods are part of statistical prediction methods that can help us to predict the right decision based on given information (features). For example, I want to know a specific buyer who came for an inquiry about our product or service will buy from us or not. If my prediction model tells us that he/she is going to make a purchase then we can put more effort and convince him/her to buy from us. For all such scenarios, we need a prediction model to make our decision more accurate.
Machine Learning methods (Algorithms) nowadays are very frequently used by us or our team to make decisions. And the reason behind using these methods is accuracy in predicting the right decision within fractions of seconds. Thanks to technology for providing high computational resources and machine learning methods for correcting our decision and mitigating our losses. Now the question arises why we need all these calculations now, earlier also we did businesses and deals with customers. The main reason behind all this point is data availability. Earlier also we did businesses and made decisions, some decisions work very well and some fail. But now if you fail today, you will be out of the market. There are so many examples present to look around. These Machine Learning methods are not optional but the necessity for us to stay in business.
I would like to talk about various Machine learning algorithms. one to select and what are their applications in real-time scenarios. Our decision in selecting the right algorithm will depend upon multiple criteria. Among them, sample size or data size is very important (number of rows in your datasets).
Here I am giving you a hierarchical chart that can help you to choose the right algorithm or estimator for your available data. Each of these algorithms is derived from different mathematical calculations and these have their advantages and disadvantages.
We start with large sample data, here a large sample means when your sample size is more than 100k. For large sample data, we will use the SGD classifier and Kernel approximation.
SGD is used mainly for large scale datasets and sparse (data which has missing values) machine learning problems. SGD can be used for text classification problems and NLP (Natural Language Processing). It is very famous among Data Scientists for building prediction models but this is not a Machine Learning model. It is an optimization method to improve the accuracy of SVM models and Logistic regression models. SVM (Support Vector Machines) is a Machine Learning algorithm that can be used for classification as well as regression problems. Logistic Regression is a traditional algorithm used only for binary class classification problems. SGDClassifier is used to fit linear classifiers and regression using convex loss function.
log?), then it results in logistic regression, and if we use SGDClassifier (loss=?hinge?), it results in Linear SVC. So in this way, we can say it is not a machine learning model but it is an optimization method. But it is very useful for large scale datasets and sparse machine learning problems because it reduces computational burden by achieving faster iteration of randomly selected subsamples to optimize the actual gradient.
The Advantage of SGDClassifier is its efficiency and ease of setup whereas its disadvantage is sensitive towards feature scaling. SGDClassifier is most suited for optimizing the Deep Learning model which is used for predicting computer vision.
Kernel Approximation — Kernel Approximation is another method which is used for large samp #
Nystroem* and RBFsamplerare very commonly used method of Kernel Approximation. You can use these methods to transform the feature variable before implementing Linear SVM or SGD Classifier. Kernel Approximation is used for the linear kernel model. you can implement a nonlinear learning model as well using SGD Classifier.
Advantage of Kernel Approximation is, it reduces the cost of learning and its Disadvantage is it is used mainly for linear SVM.
Now I am going to discuss few classification models which are used for small data sample (n<100k).
Linear SVC — Linear Support Vector Classification model is a very famous and most commonly #
It is very effective in dealing with large training datasets with multiple class levels. LinearSVC has one advantage that it does not need a very large computational resource to build a model. We will discuss more regarding the SVM method under SVC classification. It is used in decisions in a digital advertisement, e-commerce, web page categorization, text classification, and many more.
K Neighbors Classifier — K Neighbor Classifier is part of the nearest neighbor classificat #
KNeighborsClassifier makes a decision based on K nearest neighbors of each data point, where K is an integer given by the user. The value of k depends upon Data size; generally, we take large value for K to avoid noise. Distance between data point pair is calculated using Brute Force for fast computation. KNeighborsClassifier is used for uniformly distributed but if it is not uniform we use RadiusNeighborsClassifier.
In RadiusNeighborsClassifier we make a decision based on the number of a data point within a fixed radius r of each training data point, where the r value is a floating-point. The user specifies r value such that data points in the sparse neighborhoods use fewer nearest neighbors for the classification. But this method is not very effective for multiple columns training datasets.
SVC (Support Vector Classifier) is a part of SVM (Support Vector Machines) methods. SVC, NuSVC, and LinearSVC are different SVM classification methods for binary and multi-class classification problems. SVC and NuSVC are very similar methods with few differences in parameters and mathematical formulas. as parameter in fit () function but NuSVC does not have this option. LinearSVC does not have kernel options as it has a default kernel option as ’linear?.
SVM method uses a hyperplane (also called a Support Vector) for classification. This hyperplane is selected based on below two criteria.
The hyperplane should distinguish all classes correctly.
The hyperplane should be at the maximum margin from the nearest data point of each class.
These hyperplanes can be linear, nonlinear, and polynomial which can be defined by using a parameter called kernel in function.
Ensemble Classification — Ensemble Classification is a part of the Ensemble method where w #
Two different approaches we have under Ensemble method.
in this method several estimators are built independently and then we average their prediction. On averaging, the combined prediction result gives better prediction result compare to any single prediction result.
in this method base estimators are built sequentially and each estimator results in better prediction by learning from the previous one. The objective of this method is to combine several weak models and to produce a powerful ensemble.
Na’ve Bayes — Na’ve Bayes method is used for text data to train a model for predicting tex #
Na’ve Bayes classification is very fast compared to many other classification methods due to its simplified assumption. It can be training over small training datasets as well to set some estimation parameters.
You can implement Na’ve Bayes methods using Sci-Kit learn package. functions are GaussianNB, MultinomialNB, *ComplementNB, BernoulliNB,*and CategoricalNB. These functions can be used based on the training dataset and target variables.
Email Spam detection, fake and real news detection and document classification are few examples where the Na’ve Bayes method is used for.
These are the seven Machine Learning Methods which are used for Classification problems.
Questions and answers #
What is the main point? #
Any Decision that we made as a buyer or as a business person it belongs to either classification problem or regression problem.
What else did the original piece say? #
A classification problem means when you have to decide as binary (Yes/No) or multi-class (Agree/Neutral/Disagree).