|The Top Ten Machine Learning Classification Algorithms for Data Scientists|
Machine Learning classification algorithms are widely used in big data analytics, where categorizing the data helps to better understand the data.
Businesses thrive on market analytics to measure brand sentiment, analyzing how people behave online through comments, emails, online conversations, and countless other means and forms. Understanding the hidden value of text, also called reading between the lines, yields some pretty useful information. To gain an edge over competitors or catch up with forerunners, companies rely heavily on artificial intelligence and machine learning algorithms to harness the power of sentiment analysis models and accurately identify context, sarcasm or misapplied words. In addition to sentiment analysis, Machine Learning classification algorithms are widely used by data scientists in big data analytics, where categorizing the data helps to better understand the data and find patterns. Check out these top 10 Machine Learning ranking algorithms to understand how your data can drive 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 as when the output falls into either ‘yes’ or ‘no’. The data generated from the hypothesis is fit to a logarithmic function to create an S-shaped curve to predict the category of the class.
2. Naive Bayes Algorithm
It is a group of algorithms based on Bayes’ theorem, used to solve classification problems, where features are independent of each other. It is considered one of the best and easiest classification algorithms that help design ML models to make quick predictions.
3. Decision Tree Algorithm
Used for both prediction 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 Neighbor Algorithm
KNNs are supervised learning models that have different applications in pattern recognition, data mining, and intrusion detection. This algorithm is parameter independent and makes no assumptions about how the data is distributed, which means that it does not require an explicit training phase before classification, as it can classify coordinates identified by a specific attribute.
5. Support Vector Machine Algorithm
As a supervised learning algorithm, its main goal is to find a hyperplane in N-dimensional space to separate the data points into their respective categories. Mainly used for data classification and regression analysis, it is one of the precise machine algorithms that can work on smaller data sets and has been shown 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 into the category of ensemble machine learning algorithm. Used for classification and regression problems, these algorithms help when drawing decision trees to select optimal and suboptimal split points.
7. Stochastic gradient descent algorithm
These algorithms are mainly applied for linear and logistic regression analysis in large-scale machine learning problems, particularly in areas such as text analytics and natural language processing. It’s good at processing problems with billions of examples and functions. However, it lags behind in the area of speed, requiring multiple iterations along with additional hyperparameters.
8. K stands for
Also called clustering, it is an unsupervised classification algorithm used to group objects into k-groups based on their characteristics. It is an unsupervised classification algorithm that groups objects 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 approximates feature maps corresponding to certain kernels, which are used as examples in support vector machines. It uses nonlinear input transformations to serve as the basis for linear classifications and other algorithms. Although standard kernelized SVMs cannot scale well to large data sets, with an approximate kernel map, a linear support vector model can be designed.
This classification learning algorithm uses item sets to generate association rules, which are in turn used in data classification. Association rules determine how and how strongly two data points are connected. Computes associations between item sets using breadth search and Hash Tree search in an iterative process.