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Data Science Algorithms

 

It is a process or collection of rules or set to complete a task. It is one of the primary concept in, or building blocks of, computer science: the basis of the design of elegant and efficient code, data processing and preparation, and software engineering.

In data science there are mainly three algorithms are used:

  1. Data preparation, munging, and process algorithms
  1. Optimization algorithms for parameter estimation which includes Stochastic Gradient Descent, Least Squares, Newton’s Method
  1. Machine learning algorithms

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Machine Learning Algorithms

It is largely used to predict, classify, or cluster. These are the basis of artificial intelligence like as image / speech recognition and personalization of content often the basis of data products are not typically part of a core statistics curriculum .They are not generally designed to infer the underlying generative but rather to classify or predict with the most accuracy.

machine learning algorithms

  • Interpreting parameters

Statistic experts think of the parameters in their linear regression models as having real-world interpretations and typically want to be able to find the meaning in behavior or describe the real-world phenomenon corresponding to those parameters.

  • Confidence intervals

Statistic experts give confidence intervals and posterior distributions for parameters and estimators, and are interested in capturing the variability or uncertainty of the parameters.

  • The role of explicit assumptions

Statistical models make explicit assumptions about data generating processes and distributions, and you use the data to estimate parameters.

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Categories of Machine Learning

Mainly it is categorized into two categories:

  1. Supervised Learning
  2. Unsupervised Learning

 

Supervised Learning

It deals with labeled data. It is used to examine the training data and produces an inferred function that can be used for mapping new examples. In supervised learning Classification and Regression are the most general problems.

Unsupervised Learning

It deals with unlabeled data. The aim of this learning is to discover the structure in the data.

 

Three Basic Algorithms

  1. Linear Regression

It is a most important statistical method. Linear regression is used when you want to describe the mathematical relationship between two attributes or variables. When you use liner regression you will make assumption that there is a linear relationship between an outcome variable also known as label, dependent variable  and a predictor  also known as an independent variable or feature or between one variable and many other variables in which case you are modeling the relationship as having a linear structure.

 

linear regression

 

  • k-Nearest Neighbors (k-NN)

This algorithm is used when there are bunch of objects that have been classified or labeled in some way and other similar objects that haven’t gotten categorized or labeled yet, and you want a way to automatically label them. k-nearest neighbors from a dataset contains n data points and from which k is used to define that how many nearest neighbors will have an influence on the classification process. To use k-nearest neighbor (kNN), select a query point generally called P in the sample dataset and then compute it to this point.

The intuition behind k-NN is to consider the most similar other items defined in terms of their attributes, look at their labels, and give the unassigned item the majority vote. If there’s a tie, then arbitrarily choose among the labels that have tied for first.

how knn works

 

  • k-means

 It is the first unsupervised learning technique .The aim of this algorithm is to determine the meaning of the correct answer by finding clusters of data for you. It’s pretty fast compared to other clustering algorithms, and there are broad applications in marketing, computer vision (partitioning an image), or as a starting point for other models. K-means is a widely used method in cluster analysis.

The k-means algorithm takes a dataset X of N points as input, together with a parameter K specifying how many clusters to create. The output is a set of K cluster centroids and a labeling of X that assigns each of the points in X to a unique cluster. All points within a cluster are closer in distance to their centroid than they are to any other centroid.

k-means has some known issues:

  • Choose k such that: 1≤k n, where n is number of data points.
  • There are convergence issues – the solution can fail to exist if the algorithm falls into a loop, for example, and remains going back and forth between two possible solutions or in other words there is not a single unique solution.
  • Interpretability can be a problem

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