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Linear regression is one of the most widely used statistical methods available today. It is used by data analysts and students in almost every discipline. However, for the standard ordinary least squares method, there are several strong assumptions made about data that is often not true in real world data sets. This can cause numerous problems in the least squares model. One of the most common issues is a model overfitting the data. Ridge Regression and LASSO are two methods used to create a better and more accurate model. I will discuss how overfitting arises in least squares models and the reasoning for using Ridge Regression and LASSO include analysis of real world example data and compare these methods with OLS and each other to further infer the benefits and drawbacks of each method.

Ensayo El Celular

Buffer overflow attacks, Integer overflow attacks and other forms of attack.

A non-rigorous proof of the Lorentz factor and transformation in Special relativity using inertial frames of reference.

Created for Stanford Mathematics Camp 2016. A brief introduction to the Hopf Fibration for introductory topology students.

Tugas E-Business tentang G2B

The accelerated rate of growth in the amount of web applications returns as a result an increase in the traffic that web servers must handle. This aggregated traffic, in addition to the demand of the clients to be served in a real time frame, leads to the requirement of a customized way to control web related resources. All processes of the web server are tied to the control that the OS scheduler has over them, and for the default settings, the scheduler is set to handle general purpose tasks instead of being optimized forweb serving purposes. To address this issue, the use of custom settings into the scheduler will allow the daemons needed to run a web page (such as Apache, PHP and a SQL DB) to be handled by the OS as efficiently as possible. The results of the test will be the comparison in performance of a web server for different settings on the Linux Scheduler.

A short introduction of AI