Different Types of Classification in Machine Learning – Dive Deep

Machine learning involves algorithms that learn or train from examples. ML algorithms support various classification techniques. They help to assign a class label to data from the problem domain. Moreover, there are many types of tasks that you may encounter during classification in machine learning. So some specialized approaches to modeling are applicable for each classification. Read the blog below to learn the machine learning classification, algorithms, and types.

What is Classification Algorithm – Introduction

The Classification method uses supervised learning to classify fresh observations according to training data. In classification, a program makes use of the dataset or provided observations. Further, it uses this data to learn how to classify fresh observations into different categories or groups. 0 or 1, black or white, yes or no, spam or not spam, etc. are a few examples. The classification algorithm also uses labeled input data as a supervised learning technique that includes input and output data.

Thus, Classification in machine learning is a type of pattern recognition. Classification algorithms help you to find the same pattern in fresh data sets using training data.

Different Types of Classification in Machine Learning

There are four main classification tasks in ML:

1. Binary Classification

The goal of binary classification in ML is to divide the input data into two mutually exclusive categories. Thus, depending on the present issue, the training data is labeled in a binary format. It can be true or false; positive or negative; O and 1; spam or not spam, etc.

Popular algorithms useful for binary classification in machine learning include:

• Logistic Regression
• Decision Trees
• Support Vector Machine
• Naive Bayes
• k-Nearest Neighbors

2. Multi-Class Classification

The multi-class classification has at least two mutually exclusive class labels. In this classification, the goal is to find to which class a given input belongs. Hence, various algorithms for binary classification are also applicable for multi-class classification.

Popular algorithms you can use for multi-class classification include:

• k-Nearest Neighbors.
• Naive Bayes.
• Random Forest.
• Gradient Boosting.
• Decision Trees.

Classification algorithms in machine learning designed for binary classification can also be used for multi-class problems.

3. Multi-Label Classification

For each input example in these tasks, the user tries to predict 0 or more classes. Furthermore, since the input example can have several labels, there is no mutual exclusion.  A given text may contain multiple themes in various fields like auto-tagging in natural language processing. Thus, the number of items in an image can be multiple, similar to computer vision.

However, it is impossible to directly apply multi-label classification methods used for binary or multi-class classification in ML. The multi-label versions of the algorithms, which are also specialized versions of the conventional classification algorithms, include:

• Multi-label Decision Trees
• Multi-label Random Forests
• Multi-label Gradient Boosting

4. Imbalanced Classification

We can also have more of one class than the others in the training data. This is because of the uneven distribution of examples in each class for the imbalanced classification. Thus, imbalanced classification tasks are binary classification tasks. Additionally, the majority of training dataset instances belong to the normal class. But, the minority of examples belong to the abnormal class.

In these types of classification in machine learning, you can use approaches like sampling techniques. Moreover, you can also use the power of cost-sensitive algorithms.

Classification Models in Machine Learning (ML)

A classification model helps you to find useful conclusions from observed values. Therefore, if there are one or more inputs, a classification model will try to find the value of one or more outcomes. So, the outcomes are labels that you can apply to a dataset.

Several classification models in machine learning include:

• Logistic Regression: The dependent variable, y, is calculated using a model based on the independent variable, x.
• Decision Tree: A decision tree is a mechanical technique to decide by segmenting the inputs into smaller decisions. Like other machine learning classification models, it also uses mathematics.
• Random Forest: This method is comparable to the decision tree except that some randomizing is there in the questions posed. Moreover, it also aims to eliminate bias and create groups based on the most likely favorable responses.
• Naive Bayes: It works on the concept of dependent probability. However, there is no hard and fast rule for Bayes as this is just regular statistics.
• Support Vector Machines (SVMs): This approach applies a new modification to support vector classifiers that make it suitable for analyzing a non-linear decision boundary. This algorithm’s decision boundary also permits tagging the feature variable to a target variable.

Which Classification Algorithm to Choose for a Problem?

Below are some points to keep in mind while choosing classification in machine learning:

1. Identify the problem: Start with understanding the task at hand thoroughly. If it is a supervised classification case, use algorithms like Logistic Regression, Random Forest, or Decision Tree. But, if it is an unsupervised classification case, go for clustering algorithms.
2. Size of the dataset: The size of the dataset is also a crucial parameter. So, if the size of the dataset is small, go for low bias-high variance algorithms like Naive Bayes. But, if the dataset is large, the number of features will be high. Therefore, use high bias-low variance algorithms like Decision trees.
3. Prediction Accuracy: A parameter that evaluates a classifier’s performance is the model’s accuracy. It displays how closely the actual output value corresponds to the expected output value. Thus, greater precision is good, but the model should not overfit.
4. Training Time: Complex classification ML methods like Random Forests can be expensive. Additionally, learning the pattern takes longer when there are larger datasets and higher precision. However, the implementation of techniques like logistic regression is quicker and easier.
5. Number of Features: Sometimes a dataset will have a lot of features, not all of which will be important. Therefore, to determine which traits are important, one can use Principal Component Analysis or SVM algorithms. These are ideally suited for such situations.

Conclusion

In the blog, we have covered the common classification in machine learning. Predicting one of two classes is called binary classification. Whereas, multi-class classification requires selecting a class from more than two. Unbalanced classification refers to classification tasks when the distribution of cases across the classes is not equal. Multi-label classification entails predicting one or more classes for each example. Furthermore, there are various models and algorithms suitable for various scenarios.

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