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Data Analytics Hypothesis or Conclusion


Hypothesis is used in research and analytics to understand the relationship between dependent and independent variables. Hypothesis building can begin in the data exploration stage, but it becomes more mature and perfect in the conclusion or prediction phase. Key considerations of Hypothesis Building - Hypothesis are testable explanations of a problem or observation. Hypothesis building, a way to design models and predict the unknown, can be done using feature engineering. This includes Identifying meaningful features based on data domain knowledge. There are three phases to hypothesis building which are model building, model evaluation, and model deployment.

Starting 'Next For Me': Making Assumptions And Forming Hypotheses

Forbes - Tech

Next For Me publishes a weekly newsletter for 50 audiences. Along with our readers, we are hosting meetups across the country to discuss topics about working after 50 and bigger ideas like'What is our place in the world?'.

Hypothesis Testing- Test of Mean, Variance, Proportion


Hypothesis testing is used to determine whether the assumption about the value of the population parameter should be rejected or not. There are different types of hypothesis testing and different approaches to perform hypothesis testing. Let's learn about this in detail in this article. The null hypothesis is always formulated in such a way that the assumption is true. If we fail to reject the null hypothesis means no follow-up action is required.

A General Guidance of Hypothesis Testing – Towards Data Science


Hypothesis Testing, as such an important statistical technique applied widely in A/B testing for various business cases, has been relatively confusing to many people at the same time. This article aims to summarize the concept of a few key elements of hypothesis testing as well as how they impact the test results. The story starts from hypothesis. When we want to know any characteristics about a population like the form of distribution, the parameter of interest(mean, variance etc.), we make an assumption about it, which is called the hypothesis of population. Then we pull samples from population, and test whether the sample results make sense given the assumption.

Learning Multiple Tasks using Shared Hypotheses

Neural Information Processing Systems

In this work we consider a setting where we have a very large number of related tasks with few examples from each individual task. Rather than either learning each task individually (and having a large generalization error) or learning all the tasks together using a single hypothesis (and suffering a potentially large inherent error), we consider learning a small pool of {\em shared hypotheses}. Each task is then mapped to a single hypothesis in the pool (hard association). We derive VC dimension generalization bounds for our model, based on the number of tasks, shared hypothesis and the VC dimension of the hypotheses class. We conducted experiments with both synthetic problems and sentiment of reviews, which strongly support our approach.