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Analysis of a Machine Learning Algorithm to Predict Wine Quality
Published in Roshani Raut, Salah-ddine Krit, Prasenjit Chatterjee, Machine Vision for Industry 4.0, 2022
The performance of the classification models for a given set of test data is drawn by using confusion matrix. It can only be determined if the true values for test data are known. In information retrieval and classification in machine learning, precision is also called positive predictive value that is the fraction of relevant instances among the retrieved instances, while recall is also known as sensitivity that is the fraction of relevant instances that were retrieved. Both precision and recall are therefore based on relevance. In statistical hypothesis testing, a type-I error is the rejection of a true null hypothesis also known as a “false-positive” (FP) finding or conclusion; for example, an innocent person is convicted, while a type-II error is the non-rejection of a false null hypothesis also known as a “false-negative” (FN) finding or conclusion; for example, a guilty person is not convicted. The different terms used are described next:
Introduction
Published in Graham V. Weinberg, Radar Detection Theory of Sliding Window Processes, 2017
The probability of false alarm is the probability that the hypothesis H0 is rejected when it is actually true. In statistical hypothesis testing, this is known as a Type I error, or the size of the statistical test. In terms of radar detection, too many false alarms can result in a tracking algorithm missing a true target. Hence this is a critical problem, and the Pfa needs to be minimised. In practice, one usually sets this to an acceptable level, such as 10−4 or smaller. In mathematical terms the Pfa is given by Pfa=P(Z0>τg(Z1,Z2,…,ZN)|H0), $$ Pfa = P(Z_{0}> \tau g(Z_{1} ,Z_{2} , \ldots ,Z_{N} )|H_{0} ), $$
Introduction to Research
Published in Vinayak Bairagi, Mousami V. Munot, Research Methodology, 2019
Geetanjali V. Kale, J. Jayanth
Researcher defines the hypothesis and he/she needs to test that hypothesis to prove or disprove. Hypothesis defining is discussed in Section 1.6.4, this section focus on hypothesis testing. Hypothesis testing is expressed as either a null hypothesis or alternative hypothesis. If the researcher compares two methods P and Q for superiority and if proceed on the assumption that both methods are equally good, then this assumption is termed as the null hypothesis (H0). If the researcher thinks that “Method A is superior and the method B is inferior,” then it is termed as alternative hypothesis (Ha). There are Type I and Type II errors related to the null hypothesis. We may reject H0 when H0 is true. This is a Type I error. Type I error means rejection of hypothesis that should have been accepted. Type II error means accepting the hypothesis that should have been rejected. The researcher may accept H0 when H0 is not true. α and β denotes the probability of Type I error and Type II error, respectively. Curve with conditional probability of rejecting H0 as a function of population parameter and size of the sample is known as power curve of hypothesis testing. In short it is a plot of values of (1-β) for each possible value of population parameter for which the H0 is not true. This curve is defined by a function known as power function. Some of the important limitations of the discussed test are (i) the result cannot be expressed with full certainty, they are probabilistic, and (ii) testing is not a decision-making activity in itself, but the researcher should not use it in a mechanical way.
Fuzzy judgement model for assessment of improvement effectiveness to performance of processing characteristics
Published in International Journal of Production Research, 2023
Kuen-Suan Chen, Yuan-Lung Lai, Ming-Chieh Huang, Tsang-Chuan Chang
In the context of poor performance, and within index can provide information on means of improving the process performance of (Huang, Chang, and Chen 2021). In confirming the effectiveness of improvement measures, observations of increases in is the simplest and most direct approach. However, sampling error makes it possible to misjudge the effectiveness of performance improvement. Hypothesis testing is a method of statistical inference that utilises a set of sample data to draw conclusions regarding the population parameter. For this reason, hypothesis testing of is employed to develop the judgement model for the effectiveness of the performance improvement. First, we let and represent the PCI values of processing characteristic before and after improvements. When the value of is lower than required value , then it is necessary to implement a process improvement plan. Thus, hypothesis testing can be applied as follows:
Thermal and mechanical performance of cement concrete pavements containing PVC-glass mix
Published in Road Materials and Pavement Design, 2022
B. R. Anupam, L. Anjali Balan, Sunil Sharma
Statistical analysis was performed on the variation of pavement surface temperature to verify whether the results obtained are statistically significant. The statistical comparison was performed using the analysis of variance (ANOVA) method at 5% level of significance. The null hypothesis (Ho) and the alternate hypothesis (Ha) formulated were as follows: Ho: The mean of pavement surface temperature is statistically equal for the three mixes.Ha: At least one of the means of pavement surface temperature is not statistically equal.The ANOVA test results (Table 3) indicate that there is no significant change in the mean surface temperature, with the increase in PVC-glass mix dosage, throughout the day. However, while considering the temperature data of peak hours (10:00 h to 14:00 h), a significant variation in any one of the means was inferred with a p-value of 1.82 × 10−06 (Table 2). The p-value in hypothesis testing indicates the significance of the results. A smaller p-value, usually less than 0.05, is strong evidence against the null hypothesis. A p-value near to 0.05 is considered as marginal and p-values greater than 0.05 is strong evidence in favour of the null hypothesis. Hence, the mean pavement surface temperature of at least one of the mixes is significantly higher or lower than that of the remaining.
A New Approach to the Resource Allocation Problem in Fog Computing Based on Learning Automata
Published in Cybernetics and Systems, 2022
Reza Ebrahim Pourian, Mehdi Fartash, Javad Akbari Torkestani
Figures 5–9 show the simulation results in different experimental settings. In this section, the aim is to examine the validity of the obtained results using the statistical method of hypothesis testing. Hypothesis testing or significance testing is a method for testing a claim or hypothesis about a parameter in a population, using data measured in a sample. In this method, we test some hypothesis by determining the likelihood that a sample statistic could have been selected, if the hypothesis regarding the population parameter were true. Therefore, to check the validity of the obtained results, we define the following test: