Top 10 ML Classification Algorithms for Data Scientists

ML classification algorithms are used widely in big data analytics where categorizing data helps in making better sense of data

Businesses thrive on market analysis to measure brand sentiment, analyzing how people behave online via comments, emails, online conversations, and other myriad means and ways. Understanding the hidden value of text also called reading between the lines generates pretty much useful insights. To gain an edge over the competitors or to catch up with the forerunners, businesses are heavily depending on artificial intelligence and machine learning algorithms to harness the potential of sentiment analysis models, and accurately identify the context, sarcasm, or misapplied words. Apart from sentiment analysis, ML classification algorithms are used widely by data scientists in big data analytics where categorizing data helps in making better sense of data and finding patterns. Check these top 10 ML classification algorithms to understand how your data can generate those useful insights.


1. Logistic Regression:

A supervised learning algorithm is basically designed to identify the binary classification of data points, in a categorical classification such as when output falls in either of the two types, ‘yes’ or ‘no’. The data generated from the hypothesis is fitted into a log function to create an S-shaped curve to predict the category of class.


2. Naïve Bayes Algorithm:

It is a group of algorithms predicated on the Bayes theorem, used for solving classification problems, where features are independent of one another. It is considered one of the most straightforward and best classification algorithms which help in designing ML models to make quick predictions.


3. Decision Tree Algorithm:

Used for both predictions and classification in machine learning, with a given set of inputs, it is easy to map the outcomes resulting from certain consequences or decisions. They are popular for classification as they are easy to interpret and do not require feature scaling. This algorithm excludes unimportant features and data cleaning requirements are minimal.


4. K-Nearest Neighbour Algorithm:

KNNs are supervised learning models which have different applications in pattern recognition, data mining, and intrusion detection. This algorithm is parameter agnostic and does not make assumptions about how the data is distributed, which means it doesn’t require an explicit training phase before classification as it can classify the coordinates identified by a specific attribute.


5. Support Vector Machine Algorithm:

As a supervised learning algorithm, its main objective lies in finding a hyperplane in N-dimensional space to separate data points into their respective categories. Primarily used for data classification and regression analysis, it is one of the accurate machine algorithms which can work on smaller data sets and has proven to be efficient because it uses a subset of training points.


6. Random Forest Algorithm:

Also called Bootstrap Aggregation or bagging algorithm, the Random Forest algorithm falls in the category of ensemble machine learning algorithm.  Used for classification and regression problems, these algorithms come to help where the decision trees are drawn to select optimal and suboptimal split points.


7. Stochastic Gradient Descent Algorithm:

These algorithms are applied mostly for linear and logistic regression analysis, in large-scale machine learning problems, particularly in areas like text analysis and Natural Language Processing. It is good at processing problems with billions of examples and features. However, it lags in the area of speed as it requires several iterations along with additional hyperparameters.


8. K means:

Also called clusterization, it is an unsupervised classification algorithm, used for grouping objects into k-groups based on their characteristics. It is an unsupervised classification algorithm that groups object by minimizing the sum of the distances between each object and the group. K-means follows a method called Expectation-Maximization to solve classification problems.


9. Kernel Approximation Algorithm:

This module performs approximation of feature maps corresponding to certain kernels, which are used as examples in the support vector machines. It uses non-linear transformations of input to serve as the basis for linear classifications and other algorithms. Though the standard kernelized SVMs cannot scale properly to large datasets, with an approximate kernel map, a linear Support Vector Model can be designed.


10. Apriori:

This classification learning algorithm uses itemsets to generate association rules, which in turn are used in the classification of data. The association rules determine the way and the strength by which two data points are connected. It calculates the associations among itemsets using breadth-first search and Hash Tree search in an iterative process.


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