Top 10 AI Algorithms Data Scientists Need to Know in 2022

AI emulates human intellect, and AI algorithms are the programs that give data scientists the power to process it.

Artificial intelligence is making new strides in ever new domains in areas such as conceptual design, smaller devices, and multi-modal applications. Though artificial intelligence has not invaded as much expected, researchers have always been on the lookout for interesting and at times bizarre inventions. The need to remodel the existing algorithms according to the business needs leads scientists into previously unexplored areas. Recently, researchers from Stevens Institute of Technology in collaboration with the University of Chicago and Princeton University have taught an AI algorithm to model the first impressions and predict how people will be perceived based on photographs of their faces. If this example makes you curious about AI's capability in creating promising and not so weird applications, check these top 10 AI algorithms which have been put to use and will be applied in 2022 too.

1. Linear Regression Algorithm:

Linear regression algorithm is a statistical model where the relationship between a dependent and an independent variable is established. The pattern gives the analyst an idea of how the dependent variable might change for known changes in an independent variable in the future. It is a supervised learning algorithm and the pattern is established essentially by determining the character and strength of the association between the variables.

2. Logistic regression:

Unlike linear regression, logistic regression classifies data into classes, binary and multi-class logistic regression. In the simplest case, it classifies questions into 'yes' or 'no'. Though looks uncomplicated, it is not that irrelevant. In fact, there are many cases where logistic regression works better than other advanced algorithms. Logistical regression can be found in many statistical packages and tools such SAA, STATISTICA, and R. This is an easy-to-use AI algorithm, without having the need for the know-how of algorithms.

3. Linear Discriminant Analysis (LDA):

Also known as normal discriminant analysis or discriminant function analysis, it is a dimensionality reduction technique, used for supervised classification problems. When two or more classes have to be separated, the pattern in the differences should be identified, separating the classes into a higher dimension and lower dimension space. LDA algorithms are best suited for data categorisation and predictive modelling.

4. Decision trees:

It is one of the supervised learning methods which can be used to solve both regression and classification problems. Here in the decision tree model, a class label is represented by a leaf node, and attributes are represented by the internal node of a leaf. Any Boolean function with discrete attributes can be represented using a decision tree.

5. Naïve Bias:

It is primarily used for classification tasks. As the name goes, this algorithm assumes that variables that go into the algorithm are independent of each other which is not possible in the real world. which eventually helps in solving a variety of probability problems. It is relatively easy to code algorithms to make real quick predictions.

6. K-nearest neighbours:

A distance-based machine learning algorithm that uses the entire training dataset as the representation field, is used for both prediction and regression problem statements. The outcome value predictions are based not just on the data sets provided but also on the comparable values, i.e., the neighboring values. These algorithms are quick and greatly efficient when it comes to identifying the required values from the heap of datasets.

7. Learning Vector Quantization:

It overcomes the very disadvantage KNN has, i.e., the requirement of having large datasets. LVV is an advanced KNN model that uses codebook vectors to codify and define datasets. In datasets, vectors are initially random and for machines to learn from such data, it is necessary to change their values to improve prediction accuracy, which is precisely what learning vector quantization does.

8. Support Vector Machines:

A popular algorithm in the data science community, known for its robust data categorisation capabilities. Basically, used for classification problems, it is considered the best algorithm for creating the best line or decision boundary; a perfect one called a hyperplane. It does the segregation of n-dimensional space into classes so that the new data can be comfortably placed in the correct category.

9. Random Decision Forests or Bagging:

This algorithm uses a group of algorithms for obtaining the best possible route for decision-making. It evaluates the number of samples and collates the finding to come up with the most accurate output value. In this algorithm, many inferior paths are specified to determine the path which works most. In a way, it is like merging different decision trees to extract the best possible prediction.

10. Ada Boost:

Short form for Adaptive Boosting, uses a system of trees, however, unlike other tree models, it uses only one node and two leaves, known as the stump model. These algorithms improve the prediction power by improvising on algorithms by constantly boosting it with proper data until the errors are rectified and you have a correct dataset.