Titlebook: Machine Learning for Practical Decision Making; A Multidisciplinary Christo El Morr,Manar Jammal,Walid EI-Hallak Textbook 2022 The Editor(

BARK

Introduction to Machine Learning,prosperous and more accurate insights with software that learns from experience and adapts automatically to match the needs of its tasks [1]. Formerly, the programmer decided how the system would work by manually writing the code. Today, we do not write programs but rather collect data consisting of

嘴唇可修剪

Statistics,ive statistics and inferential statistics. Descriptive statistics involve collecting, organizing, and summarizing a given dataset, whereas inferential statistics relate to concluding a population from the results obtained in a sample. In the following chapter, we will give an overview of some common

Blood-Clot

Overview of Machine Learning Algorithms,, processed, and analyzed. Well-defined knowledge can be represented in an accurate manner, such as a mathematical formula or a certain set of rules [1]. Knowledge can also be modeled, where a model permits us to explain reality, classify objects, and predict a value (or if an event will occur) know

Critical

Data Preprocessing,on [1]. It is an important step before processing and usually entails reformatting, adjusting, and integrating datasets to improve the information contained within them. Even though data preprocessing can be an onerous task, it is necessary as a precondition for putting data into context and reducin

carbohydrate

Agronomy

expunge

Logistic Regression,odeling technique for binary outcomes (i.e., yes/no, true/false, 1/0). Such outcomes are needed in many domains: public health officials might want to know the likelihood that a person will contract COVID-19 if she is a doctor in Ontario; a hospital would like to know if a discharged patient is more

善辯

Decision Trees, label the instance as belonging to a class. The decision tree is our first approach to solve classification problems. However, decision trees can perform regression too, hence their name classification and regression trees (CART). The random forests that we will encounter in a later chapter are pow

碌碌之人

表示向下