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Articles by Mithilesh Pradhan
Page 3 of 5
Ensemble Classifier | Data Mining
Introduction Ensemble Classifiers are class models that combine the predictive power of several models to generate more powerful models than individual ones. A group of classifiers is learned and the final is selected using the voting mechanism. Data mining is the process of exploring and analyzing large datasets to find and explore important patterns, relationships, and information. The extracted information can then be used to solve business problems, predict trends and generate strategic plans by organizations. Ensemble classifiers are used in data mining to perform such tasks. Why do we need ensemble classifiers? Ensemble models(classifiers) can solve many problems and ...
Read MoreUnderstanding Types of Mean
Introduction The average value of a set of data points, observations, or values is known as the Mean of the data. It is the measure of the central tendency. Mathematically, the mean is obtained by dividing the sum of values by the number of values or observations. It is also called the expected value. The mean itself is not restricted to this simple form but has different types such as the arithmetic mean, geometric mean, harmonic mean, and weighted average. It is given mathematically as, $$\mathrm{Mean=\frac{\sum x}{N}}$$ where, x = set of observations N = number of observations Different ...
Read MoreTypes of Regression Techniques in Machine Learning
Introduction Regression is the technique of predictive modeling to analyze the relationship between the independent variable and the dependent variable. The relation between the target(dependent variable) and the independent variable may be either linear or non-linear. The target is always continuous value and regression is widely used in forecasting, understanding cause and effect as well as in predictive analysis. In this article let us explore various regression techniques available. Regression Techniques Linear Regression − It is the simplest of all regression techniques. In Linear Regression the independent and the target variable are linearly related or dependent on each ...
Read MoreImpacts of Artificial Intelligence in everyday life
In today’s date, Artificial Intelligence has impacted our lives way in that nothing else could have impacted. It has changed how our daily jobs are done and has involvement in major areas of our industry, lives, and almost everything. Here in this article, we are going to see some areas in our lives where Artificial Intelligence has significantly played its role. Health Industry Healthcare is a growing and crucial industry in the 21st century. Not only it is providing a better life to people but also saving millions of lives every day. We just can't overlook the health aspect of ...
Read MoreNon-Negative Matrix Factorization
Introduction Non-Negative Matrix Factorization (NMF) is a supervised algorithm used to represent data into lower dimensions which reduces the number of features while preserving enough basic information to construct the original matrix from the reduced feature space. In this article, we will be going explore more about NMF and how it can be useful. Non-Negative Matrix Factorization NMF is used to reduce the dimensions of the input matrix or corpus. It uses factor analysis which gives less importance to less relevant words. The decomposition of the original matrix(which is a non-negative matrix) thus creates a product of two non-negative coefficients ...
Read MoreImplementation of Particle Swarm Optimization
Introduction The Particle Swarm Optimization algorithm is inspired by nature and is based on the social behavior of birds in a flock or the behavior of fish and is a population-based algorithm for search. It is a simulation to discover the pattern in which birds fly and their formations and grouping during flying activity. Particle Swarm Optimization Algorithm In the PSO Algorithm, each individual is considered to be a particle in some high-dimensional search space. Inspired by the social and psychological behavior of people, which they tend to copy from other people's success, similar changes are made to the particles ...
Read MoreHow to Become an RPA Developer?
Introduction RPA stands for Robotic Process Automation. An RPA developer is a person who designs, maintains, builds, and implements RPA systems. In an organization, an RPA developer has the role to create optimized workflow processes and work cross-functionally with operations and business analysts. Scope of an RPA Developer Today the world is moving towards automation where organizations maximum repetitive processes to be automated as much as possible. Thus the demand for highly skilled RPA professionals has gained pace. With the right skill set, RPA developers can fill the in the respective domains. An RPA developer is essentially a software developer ...
Read MoreExploratory Data Analysis (EDA) - Types and Tools
Introduction Exploratory Data Analysis (EDA) is the process of summarization of a dataset by analyzing it. It is used to investigate a dataset and lay down its characteristics. EDA is a fundamental process in many Data Science or Analysis tasks. Different types of Exploratory Data Analysis There are broadly two categories of EDA Univariate Exploratory Data Analysis – In Univariate Data Analysis we use one variable or feature to determine the characteristics of the dataset. We derive the relationships and distribution of data concerning only one feature or variable. In this category, we have the liberty to use either ...
Read MoreDocument Retrieval using Boolean Model and Vector Space Model
Introduction Document Retrieval in Machine Learning is part of a larger aspect known as Information Retrieval, where a given query by the user, the system tries to find relevant documents to the search query as well as rank them in order of relevance or match. They are different ways of Document retrieval, two popular ones are − Boolean Model Vector Space Model Let us have a brief understanding of each of the above methods. Boolean Model It is a set-based retrieval model.The user query is in boolean form. Queries are joined using AND, OR, NOT, etc. A document ...
Read MoreDeepWalk Algorithm
Introduction The graph is a very useful data structure that can represent co-interactions. These co-interactions can be encoded by neural networks as embeddings to be used in different ML Algorithms. This is where the DeepWalk algorithm shines. In this article, we are going to explore the DeepWalk algorithm with a Word2Vec example. Let us learn more about Graph Networks on which the core of this algorithm is based. The Graph If we consider a particular ecosystem, a graph generally represents the interaction between two or more entities. A Graph Network has two objects – node or vertex and edge. ...
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