Baseline results simply mean the simplest form of predictions that can be derived from an algorithm (John, 2015). The baseline result for a problem is usually unique and is determined from the model that is used. Nevertheless, the baseline result may be a known prediction or maybe just one unique answer. It is therefore imperative to understand the basis of the baseline and more to this have a reference point from where the necessary comparisons can be made. The major reason for using baseline is to determine the load requirements as well as the system performance (John, 2015). However, there are numerous pros as well as cons that are associated with the performance of the system.
To begin with, baseline system performance is very good in measuring a system’s energy performance. The system works by comparing energy usage between two different periods. The baseline therefore, provides a correlation between two periods and determines whether the usage is good or bad.
Secondly, the baseline system performance is very useful in determining the savings that can be accrued from energy usage. It is very good in energy projects and therefore is used for the long-term monitoring of the total costs that may be accrued when there are different projects underway. Moreover, the baseline can manipulate the long-term energy usage of a system and therefore very useful in determining the cost efficiency that is associated with a project.
However, there is baseline system performance results that may be very bad. Some of these scenarios are centered on the inadequacy of servers, datasets, and services (Brownlee, 2014). To determine the best baseline results, it is important for all the aforementioned to be present. Moreover, the tests conducted should be combined as well a separate.
Bayesian classification refers to the methodology employed in improving the accuracy of the datasets that are available by reducing and removing the redundant qualities (Kumar & Kumar, 2011). It is therefore very imperative to consider the different weights of the data sets that are available since they are important in the value weighing process. Majorly, the weights are assigned to the different values of the features that are available.
The design of a Bayes classifier has been proposed by many authors who have based the decisions on the tree diagram. One of these methods is DTNB which is based on the dependency of the different features (Kamalloo & Rafiei, 2018). By understanding the dependencies of the different features from the tree diagram, the depth of the features can be used to determine the testing that may be conducted. In line with the testing, there is the stabilization that occurs thereafter in order to provide and categorize the estimations. Consequently, the features that cannot be observed in the feature tree have no significant weight while those observed have varying weights.
The information-theoretic method is one of the most import methods that are used in feature selection. The process of feature selection prevents ambiguity in the selection of data because it provides a platform from where the different data subsets may be separated. Moreover, the method selects the features of data that have the best details. The method is based on the mutual informatics to describe the function that may be used in the evaluation. By using the values that are derived from instances that may not be classified, there are the empirical requirements that call for separation as well as classification. However, the separation has to consider the most viable data and this is the reason why there is benchmarking.
There has been continued research on unsupervised learning technologies, especially for language processing. However, the type of unsupervised learning that is considered has to be in line with the algorithm (Kamalloo & Rafiei, 2018). The algorithm generally determines the data set that is available for processing and therefore an important aspect in the end evaluation results.
To begin with, there is the use of probability values on the data in order to determine the performance. However, the method is best suited for the learning methods that are highly dependent on probability (Kamalloo & Rafiei, 2018). Assigning values to unsupervised data depends on the probability value and as such, determines how the interest is distributed. This is the basis of language modeling where an increase in probability determines the distribution of data which in turn determines the levels of interest. It is therefore important to supervise the output.
Furthermore, the process of putting together algorithms leads to difficulty in the evaluation. However, the difficulty is overcome by the use of metrics in unsupervised learning. Metrics are based on the information available in determining the separation as well as the compactness of the data (John, 2015). Determining the extent of separation can also be achieved through the use of external metrics. Nevertheless, the metrics are used in statistical analysis of the available and clustered data and therefore very important in the unsupervised learning technology.
These unsupervised learning are also used in determining the workflow by providing a proper definition and analysis of the functions that are external to the available function. It is One of the methods that may be used is clustering the data that is available and following it up with the creation of an extrinsic set of data. Creation of the extrinsic dataset is mainly achieved through labeling as well as testing the accuracy. This is well determined by the dimensionality method of data estimation.
However, there are a number of challenges that are associated with unsupervised learning because of the unavailability of data. The major advantage of the method is that it only requires only one set of data during the process of modeling but this brings a challenge during the evaluation. All that stated, the inability to compare the outputs against the gold standards is therefore brought about by the inability to label the output data. If there are no models in the method of learning, then there are no labels on the output. Therefore, there is a need for supervision and comparison to the gold standards available.
Brownlee, J. (2014, November 05). How To Get Baseline Results And Why They Matter. Retrieved from https://machinelearningmastery.com/how-to-get-baseline-results-and-why-they-matter/
John, M. (2015, August 19). What is a Baseline? And Why It’s Critical to Good Energy Management. Retrieved from https://www.energysmart.enernoc.com/what-baseline-and-why-it%E2%80%99s-critical-good-energy-management
Kamalloo, E., & Rafiei, D. (2018). Proceedings of the 2018 World Wide Web Conference. A Coherent Unsupervised Model for Toponym Resolution. Lyon.
Kumar, G., & Kumar, K. (2011). An information theoretic approach for the feature. SECURITY AND COMMUNICATION NETWORKS.