A Classification of Machine Learning Algorithm

One of the most significant areas in Artificial Intelligence is Machine Learning. It is further separated into supervised and unsupervised learning, which can be related to labelled and unlabelled data analysis and data prediction, respectively. Regression and Classification are two more forms of business challenges in Supervised Learning.

Classification is a machine learning algorithm in which the input is labelled data and the output is predicted into a class. Binary classification is used when there are just two classes. Multi-Class Classification is used when there are more than two classes. Both types of classification are common in real-world circumstances.

We’ll look at a few different types of classification algorithms, as well as their benefits and drawbacks, in this article. There are numerous categorization algorithms available; however, we’ll concentrate on the following five:

1. Logistic Regression
2. K Nearest Neighbour
3. Decision Trees
4. Random Forest
5. Support Vector Machines

1.     Regression Model

Despite the name, this is a Classification Algorithm, not a Regression Algorithm. A statistical method for classifying data in which one or more independent variables or features determine an outcome that is measured with a variable (TARGET) that has two or more classes is known as logistic regression. Its primary purpose is to find the model that best describes the connection between the target variable and the independent variables.

Pros

• It is simple to implement, interpret, and train because it makes no assumptions and is quick at classifying
• Multi-class classification is possible
• It is less prone to overfitting in low-dimensional datasets, but it does overfit in high-dimensional datasets

Cons

1)     When the number of observations is more than the number of features, the model overfits

2)     This function can only be used with discrete functions

3)     Non-linear issues are impossible to solve

2.     K Nearest Neighbours

The KNN method predicts which cluster a new data point will fall into using the technique of ‘feature similarity’ or ‘nearest neighbours.’ The following are a few steps that will help us better grasp how this algorithm works.

Step 1: To implement any machine learning technique, we require a cleansed data set that is suitable for modelling. Let’s pretend we already have a cleaned dataset divided into training and testing data sets.

Step 2: Now that we have the data sets, we must determine the value of K (integer), which indicates how many nearby data points we must consider when implementing the algorithm. In the latter phases of the article, we will learn how to calculate the k value.

Step 3: is an iterative process that must be repeated for each data point in the dataset.

Using any of the distance metrics, calculate the distance between test data and each row of training data.

• Distance in Euclidean space
• Manhattan is a long way away
• Hamming distance
• Murkowski distance

Many data scientists prefer to use the Euclidean distance, but we’ll learn more about the meaning of each one later in this post.

We must sort the data using the distance measure we established in the previous step. Select the top K rows from the sorted converted data.

The test point will then be assigned a class based on the most common class of these rows. Step 4 – Finish

Pros

• It’s simple to use, comprehend, and
• Time to calculate is
• There are no data
• Predictions with high
• Versatile – It may be used to solve both classification and regression problems in the business
• It can also be used to solve multi-class
• At the Hyperparameter Tuning stage, we only have one Hyperparameter to

Cons

• Because the technique maintains all of the training data, it is computationally intensive and requires a lot of memory.
• As the number of variables grows, the algorithm becomes slower.
• It is extremely sensitive to non-essential traits.
• Dimensionality’s Curse
• Choosing the most appropriate K value. An unbalanced dataset will result in a problem.
• Data with missing values is also problematic.

3.  Decision Trees

Because it can handle both numerical and categorical data, decision trees can be used for both classification and regression. As the tree grows, it splits down the data set into smaller and smaller sections or nodes. A decision tree’s output contains decision and leaf nodes, each of which has two or more branches and reflects a choice. The root node is the topmost node that corresponds to the best predictor.

Pros

• Simple to comprehend
• Visualization is
• fewer data to interpret
• It is capable of handling both numerical and category

Cons

• Sometimes it’s difficult to
• Changes in input data make it

4.  Random Forests

Random forests are a type of ensemble learning that can be applied to classification and regression. In Regression or Majority voting in Classification problems, it constructs numerous decision trees and outputs the findings by taking the mean of all decision trees. A Forest is a group of trees, as you can see from the name.

Pros

• Large datasets are no
• The importance of variables will be
• Missing values are not a

Cons

• It’s a closed-loop
• Complex algorithms and slow real-time prediction

5.  Support Vector Machines

A support vector machine represents a data set as points in space that are split into categories by a distinct gap or line that stretches as far as possible. The additional data points are now mapped into the same area and categorized as belonging to one of several categories based on which side of the line or separation they land on.

Pros

• In high-dimensional spaces, it works
• It is a memory economical algorithm since it only uses a subset of training data points in the decision

Cons

• No probability estimates will be
• Cross-validation can be used to derive probability estimates; however, it is time

Conclusion

In this article, we reviewed the five classification algorithms, their brief definitions, and their advantages & disadvantages. We’ve only examined a few algorithms; others are more useful, such as Naive Bayes, Neural Networks, and Ordered Logistic Regression. Because it is impossible to predict which algorithm would perform best for whatever situation, the best practice is to try out a few and then choose the best model based on assessment metrics.