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Hierarchical Clustering

What is Hierarchical Clustering? An Introduction to Hierarchical Clustering

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Contributed by: Satish Rajendran LinkedIn Profile: https://www.linkedin.com/in/satish-rajendran85/ What is Hierarchical Clustering Clustering is one of the popular techniques used to create homogeneous groups of entities or objects. For a given set of data points, grouping the data points into X number of clusters so that similar data points in the clusters are close to each […]

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model evaluation metrics

Famous Machine Learning Metrics | Model Evaluation Metrics for Machine Learning

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Introduction Model and Performance Matrix Match Type of cut off approach in classification model Regression model performance parameters Classification model performance parameters Model Stability Contributed by: Rishabh Pandey LinkedIn profile: https://www.linkedin.com/in/rishabh1409/ Introduction Whenever you build a statistical or Machine Learning model, all the audiences including business stakeholders have only one question, what is model performance?

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Maximum likelihood estimation

Maximum Likelihood Estimation: What Does it Mean?

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Contributed by: Venkat Murali LinkedIn Profile: https://www.linkedin.com/in/venkat-murali-3753bab/ The maximum likelihood estimation is a method that determines values for parameters of the model. It is the statistical method of estimating the parameters of the probability distribution by maximizing the likelihood function. The point in which the parameter value that maximizes the likelihood function is called the maximum

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goodness of fit test

Understanding Goodness of Fit Test, Definition | What is Goodness of Fit?

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Contributed by: Rahul SinghLinkedIn Profile: https://www.linkedin.com/in/rahul-singh-3b213117/ What is the goodness of fit? A goodness-of-fit is a statistical technique. It is applied to measure “how well the actual(observed) data points fit into a Machine Learning model”. It summarizes the divergence between actual observed data points and expected data points in context to a statistical or Machine

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principal component analysis

Understanding Principal Component Analysis and their Applications

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While working on different Machine Learning techniques for Data Analysis, we deal with hundreds or thousands of variables. Most of the variables are correlated with each other. In such cases, fitting the model to the dataset results in poor accuracy of the Model. Principal Component Analysis and Factor Analysis techniques are used to deal with such

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prerequisites for machine learning

Pre-requisites for a Machine Learning Career | Best option to start Machine learning Career

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Machine Learning (ML) is an interdisciplinary field comprising of Computer Science, Electronics and Communication, Neuroscience, Psychology, Economics, Statistics and many more exciting fields of science. The vast inclusion of domains gives everyone an equal opportunity to be a part of this ever-growing field of science. There are several applications of Machine Learning and it is

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An Introduction to Central Limit Theorem | What is Central Limit Theorem

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Central Limit Theorem In machine learning, statistics play a significant role in achieving data distribution and the study of inferential statistics. A data scientist must understand the math behind sample data and Central Limit Theorem answers most of the problems. Let us discuss the concept of the Central Limit Theorem. It assumes that the distribution

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