Selecting the Right Type of Algorithm for Various Applications - Phdassistance
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This document discusses key factors to consider when selecting a machine learning algorithm for a problem. It covers the main types of algorithms - supervised, unsupervised, and reinforcement learning. When choosing an algorithm, it is important to understand the data by examining patterns, size, features, and whether the data is input, output, numeric or categorical. The required accuracy and speed also impact the choice, with simpler algorithms being faster but less accurate. Parameters like the number of dimensions and features can increase processing time for some algorithms.
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Selecting the Right Type of Algorithm for Various Applications - Phdassistance
- 1. An Academic presentation by Dr. Nancy Agnes, Head, Technical Operations, Phdassistance Group www.phdassistance.com Email: [email protected]
- 2. TODAY' S DISCUSSION Introduction Understanding the Data Required Accuracy Speed Parameters
- 3. INTRODUCTION M achine learning algorithms may be classified mainly into three main types. Supervised learning constructs a mathematical model from the training data, including input and output labels. T he techniq ues of data categorization and regression are deemed supervised learning. Contd...
- 4. In unsupervised learning, the system constructs a model using just the input characteristics but no output labeling. 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. UNDERSTANDING T HE DATA The f i rst and primary stage in determining an a lg orithm is the understanding of your data. One needs to acquaint themselves with data before thinking about the various algorithms. 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 than others with greater data (Mahfouz et al., 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. Figure 1:Types of Machine Learning Algorithms
- 8. The feature of parameter. The data is another way the data is created, and whether it is linear to the data must be considered. Then maybe a linear model is most such as regressions or However, if is suited, SVM. more complicated your data then complicated algorithms more like 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 or output. 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. 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 method to estimate a response observation near to the from a given right response (Garg, 2020 ). Contd... REQUIRED ACCURACY
- 11. Sometimes a correct reply to our target application is not essential. by adopting If the approximation is strong an may enough, approximate considerably 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. S P E E D Sometimes users have to choose between speed and accuracy in order to decide on an algorithm. Typically, more precision takes longer to achieve, over a longer t imeline, while faster processing has less accuracy. The incredibly simple algorithms l ike Naïve Bayes and Logistic regression are used often since they' re simple, quick to run algorithms. C o n td ...
- 13. Using more advanced techniques l ike support vector machine l earning, neural networks, and random forests, might take a lot longer to learn, and would also give higher 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. P A R A M E T E R S The parameters will impact how the algorithm behaves . Options that alter the algorithm' s behavior, such as tolerance for error or the number of i terations. 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. C o n td ...
- 15. the number The greater dimensions , However, an the more algorithm time with of parameters the it takes to process numerous parameters model' s and t rain. means the method is adaptable. Machine learning addresses measurable 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 numerous features.
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