What are The Top 10 Algorithms in Machine Learning?

August 21, 2018

Machine learning is the essential part of the developing technology of Artificial Intelligence. It analyses enormous amounts of data and comes at customized predictions which can help the user to deal logically with an overload of information. A student of Machine Learning course must be aware of the need of making algorithms since these are what enhance the self-teaching capacities of the system. There are three primary techniques to design an algorithm- supervised, unsupervised and reinforced.

Also Read : What is The Easiest Way To Learn Machine Learning?

Here is a list of the top 10 algorithms which every Machine Learning student must know about –

  1. Decision Tree is one of the most comfortable supervised structures that is very useful to form deep connections and is based on questions in Boolean format. The fabric is systematic and easy to understand, and it is beneficial to determine model decisions and outcomes of chance-events.
  2. Naive Bayes is a simple and robust algorithm for classification. The “naive” term implies that it assumes every variable to be independent which can turn out as impractical sometimes. However, it is a great tool which is successfully used in spam detection, face recognition, article classification and other such operations.
  3. Linear Discrimination Analysis or LDA is another simple classification algorithm. It takes the mean and variance values across classes and makes predictions based on the discriminated value assuming that the data has a Gaussian curve.
  4. Logistic Regression is a fast and effective statistical model best used for binary model classifications. Some real-world applications of this algorithm are scoring credit points, understanding rates of success in market investments and earthquake detection.
  5. Support Vector Machines or SVMs are a well-known set of algorithms which is binary based. The principle is to find the best separation of variables in a hyperplane. The support vectors are the points which define the hyperplane and construct the classifier. Some successful sites to try this algorithm is image classification and display advertising.
  6. Clustering algorithm follows the unsupervised technique, and it works on the principle of determining the more similar characteristics of nearby parameters to patch themselves up in a set cluster or group. There are different types of clustering algorithms such as centroid-based algorithms, dimensionality reduction and neural networks.
  7. Linear regression is a very well understood form of the algorithm which works on quite the same mathematical formula of a linear equation in two dimensions. It is a well-practised algorithm to determine the relationship between two variables and can be used to remove unnecessary variables from your target function.
  8. Ensemble methods are a group of learning algorithms working on the principle of predictive analysis. They construct a chain of classifiers such that the final structure is established to be a superior one. They are very efficient regarding averaging away with biases in poll decisions, and the algorithms are entirely immune to the problem of over-fitting.
  9. Principal component analysis or PCA employs an orthogonal transformation to convert relatable variables into a set of uncorrelated variables called principal components. Some essential uses of the method are compression and data simplification
  10. Independent Component analysis or ICA is a statistical method to determine underlying data which come obscured in data signals and variables. Relative to PCA, this is a more powerful method and works well with applications like digital images, documented databases and psychometric detections.

While no algorithm in itself can be guaranteed for a specific result, it’s always ideal to test multiple algorithms cumulatively. The ultimate task of an algorithm is to create a target function which can process a set of input into detailed output data.

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