Selecting the Right Type of Algorithm for Various Applications - Phdassistance

1. An Academic presentationby Dr. Nancy Agnes, Head, Technical Operations, Phdassistance Group www.phdassistance.com Email:info@phdassistance.com

2. TODAY'S DISCUSSION Introduction Understanding the Data Required Accuracy Speed Parameters

3. INTRODUCTION Machine learningalgorithmsmay be classified mainly into three maintypes. Supervised learning constructs a mathematical model from the training data, including input and outputlabels. The techniq ues of datacategorization and regression are deemed supervised learning. Contd...

4. In unsupervised learning, the system constructs a model using just the input characteristics but no outputlabeling. The classifiers are then trained to search the dataset for a specific pattern. Examples of uncontrolled learning algorithms including clustering and segmentation. In reinforcement learning, the model learns to complete a task in reinforcement learning by executing a number of actions and choices that it improves itself and then understands from the information from these actions and decisions (Lee & Shin,2020).

5. THEDATA UNDERSTANDING The f i rst and primary stage in determining an algorithmis the understanding of yourdata. One needs to acquaint themselves with data before thinking about the variousalgorithms. One easy approach of doing this is to view data and attempt to detect patterns in them, to watch their behavior and especially their size. Contd...

6. The size of the data is an important parameter. Some algorithms do better thanotherswithgreaterdata(Mahfouzetal.,2020). For instance, algorithms with higher bias or lower variance classification are more effective than lower bias or higher variance classifications in limited training datasets (Richter et al.,2020). For instance, Naïve Bayes will do better than kNN if the training data is smaller.

7. Figure1:TypesofMachineLearningAlgorithms

8. data is another way the data is The feature of parameter. The created, and whether it is linear to the data must be considered. Then maybe a linear model is most suited, SVM. more such as regressions or However, if is your data then more like complicated complicated algorithms Random forest may be required. Contd...

9. The features being linked or sequential also requires specific type of algorithms. The type of data is an important parameter ( Vabalas et al., 2019 ) . The data maybe classified into input oroutput. Use a supervised learning method i f the input data are labeled; otherwise,unsupervised algorithm must be used. If numerical, on the other hand, the output is then regression will be used, but if it is a collection of groups, it is an issue of clustering. Contd...

10. REQUIREDACCURACY In the next step, i t should be decided whether or not accuracy is important for the issue one is attempting to address. The accuracy of an application refers to the capacity of an individual methodto estimate a response observation near to the from a given right response (Garg, 2020). Contd...

11. Sometimes a correct reply to our target application is not essential. If the approximation is strong an may enough, approximate considerably by adopting model, we reduce the training and processing time. Approximation approaches, such as linear regression data, prevent or of non-linear do not execute data overfitting.

12. SPEED Sometimes users have to choose between speed and accuracy in order to decide on analgorithm. Typically, more precision takes longer to achieve, over a longer t imeline, while faster processing has lessaccuracy. The incrediblysimple algorithms like Naïve Bayes and Logistic regression are used often since they' re simple, quick to runalgorithms. Contd...

13. Using more advanced techniques l ike support vectormachine l earning, neural networks, and random forests, might take a lot longer to learn, and would also givehigher accuracy. Therefore, the question is how much is the project worth, Is t ime more important or the accuracy. I f i t is t ime, simpler methods must be used, while i f accuracy is more important, then one has to go with more sophisticated ones.

14. PARAMETERS The parameterswill impact how the algorithm behaves . Options that alter the algorithm' s behavior, such as tolerance for error or the number of iterations. For as many parameters as the data has, t ime required to process the data t raining and processing t ime is f requently proportional. Contd...

15. The greater dimensions , However, an of parameters the it takes to process numerous parameters model's and t rain. means the the number the more algorithm time with method isadaptable. Machine learningaddressesmeasurable variables. Having more features might slow down certain algorithms, therefore this causes them to take a lengthy t ime to train. So long as the issue has a large feature set, one should choose an algorithm such as SVM, which is best suited to those with numerousfeatures.

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