scholarly journals Prediction Modeling Methodology

2018 ◽  
pp. 101-120 ◽  
Author(s):  
Frank J. W. M. Dankers ◽  
Alberto Traverso ◽  
Leonard Wee ◽  
Sander M. J. van Kuijk
Keyword(s):  
Machine Learning ◽  
Predictive Model ◽  
Machine Learning Techniques ◽  
Prediction Modeling ◽  
Modeling Methodology ◽  
Learning Techniques ◽  
Regression Techniques

AbstractIn the previous chapter, you have learned how to prepare your data before you start the process of generating a predictive model. In this chapter, you will learn how to make a predictive model using very common regression techniques and how to evaluate the performance of a model. In the next chapter we will then look at more advanced machine learning techniques that have become increasingly popular in recent years.

scholarly journals Intersections of machine learning and epidemiological methods for health services research

2020 ◽  
Author(s):  
Sherri Rose
Keyword(s):  
Machine Learning ◽  
Health Care ◽  
Health Services Research ◽  
Health Services ◽  
Machine Learning Techniques ◽  
Epidemiological Methods ◽  
Learning Tools ◽  
Services Research ◽  
Learning Techniques ◽  
Regression Techniques

Abstract The field of health services research is broad and seeks to answer questions about the health care system. It is inherently interdisciplinary, and epidemiologists have made crucial contributions. Parametric regression techniques remain standard practice in health services research with machine learning techniques currently having low penetrance in comparison. However, studies in several prominent areas, including health care spending, outcomes and quality, have begun deploying machine learning tools for these applications. Nevertheless, major advances in epidemiological methods are also as yet underleveraged in health services research. This article summarizes the current state of machine learning in key areas of health services research, and discusses important future directions at the intersection of machine learning and epidemiological methods for health services research.

scholarly journals Analytical Statistics Techniques of Classification and Regression in Machine Learning

2020 ◽  
Author(s):  
Pramod Kumar ◽  
Sameer Ambekar ◽  
Manish Kumar ◽  
Subarna Roy
Keyword(s):  
Machine Learning ◽  
Linear Regression ◽  
Machine Learning Techniques ◽  
Statistical Machine Learning ◽  
Building Models ◽  
Learning Techniques ◽  
Implementation Techniques ◽  
Regression Techniques ◽  
The Common ◽  
Classification And Regression

This chapter aims to introduce the common methods and practices of statistical machine learning techniques. It contains the development of algorithms, applications of algorithms and also the ways by which they learn from the observed data by building models. In turn, these models can be used to predict. Although one assumes that machine learning and statistics are not quite related to each other, it is evident that machine learning and statistics go hand in hand. We observe how the methods used in statistics such as linear regression and classification are made use of in machine learning. We also take a look at the implementation techniques of classification and regression techniques. Although machine learning provides standard libraries to implement tons of algorithms, we take a look on how to tune the algorithms and what parameters of the algorithm or the features of the algorithm affect the performance of the algorithm based on the statistical methods.

Review on Various Machine Learning and Deep Learning Techniques for Prediction and Classification of Quotidian Datasets

2020 ◽  
pp. 296-323
Author(s):  
Anisha M. Lal ◽  
B. Koushik Reddy ◽  
Aju D.
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Learning Algorithm ◽  
Machine Learning Algorithms ◽  
Training Data ◽  
Supervised Machine Learning ◽  
Machine Learning Techniques ◽  
Learning Techniques ◽  
Supervised Methods ◽  
Regression Techniques

Machine learning can be defined as the ability of a computer to learn and solve a problem without being explicitly coded. The efficiency of the program increases with experience through the task specified. In traditional programming, the program and the input are specified to get the output, but in the case of machine learning, the targets and predictors are provided to the algorithm make the process trained. This chapter focuses on various machine learning techniques and their performance with commonly used datasets. A supervised learning algorithm consists of a target variable that is to be predicted from a given set of predictors. Using these established targets is a function that plots targets to a given set of predictors. The training process allows the system to train the unknown data and continues until the model achieves a desired level of accuracy on the training data. The supervised methods can be usually categorized as classification and regression. This chapter discourses some of the popular supervised machine learning algorithms and their performances using quotidian datasets. This chapter also discusses some of the non-linear regression techniques and some insights on deep learning with respect to object recognition.

A Predictive Model for Hypertension Diagnosis Using Machine Learning Techniques

2019 ◽  
pp. 139-152 ◽  
Author(s):  
Muhammad Ali Javed Tengnah ◽  
Raginee Sooklall ◽  
Soulakshmee Devi Nagowah
Keyword(s):  
Machine Learning ◽  
Predictive Model ◽  
Machine Learning Techniques ◽  
Learning Techniques ◽  
Hypertension Diagnosis

scholarly journals Stock Market (NIFTY) Forecasting using Machine Learning Analysis on Option Chain

2021 ◽  
Vol 9 (5) ◽  
pp. 80-83
Author(s):  
Saurabh Gupta ◽  
Vaishali Vaishali ◽  
Raghuvansh Tahlan ◽  
Navya Sanjna Joshi ◽  
Ritvik Agarwal
Keyword(s):  
Machine Learning ◽  
Stock Market ◽  
Language Processing ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Learning Techniques ◽  
Long Time ◽  
Direction Of Movement ◽  
Regression Techniques ◽  
Learning Analysis

Stock market prediction is a long-time intriguing topic to researchers from different fields. Stock market data is extremely volatile and hence laborious to model. In particular, innumerable studies have been conducted to predict the movement of stock market using Machine Learning algorithms such as Regression Techniques, Time Series Forecasting, Indices Modelling, Natural Language Processing and more, but there is still room for improvement. Also, Option chain and Options have been the subjects that not many have ventured into, leading us to this subject. Mainly, NIFTY and BANKNIFTY Options account for 70% of total derivatives traded and much more turnover than all stocks combined. This research paper attempts to figure out the utility of Option Chain in predicting the direction of movement in NIFTY. We have tried how different features from Option chain can be extracted, and the resulting problem can be solved using Machine Learning techniques and Deep Learning techniques.

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