Any kind of training is considered successful after calculating quantitative values to measure efficiency. Many such types of factors are to be considered while selecting and training a model. Understanding and choosing a ML Algorithms that suits best as per the use case is a very crucial step.
There is no straight forward rule for selecting a model. Selection of model depends upon factors such as the use case, problem statement, input type, output type, computational availability, number of observations, etc.
factors to select ML Algorithms
1. Understanding Data
Generally, it is recommended to gather a huge amount of data to get better accuracy. However, it is not always possible to gather such a massive amount of data. Hence choosing an algorithm that has high bias and low variance such as Linear regression, Naïve Bayes can be very effective.
On the other hand, if the training data is sufficiently large then choosing an algorithm that has low bias and high variance such as KNN, Decision trees can be a smart choice.
2. Accuracy
Technically the definition of accuracy is “the degree to which the result of a measurement, calculation, or specification conforms to the correct value or a standard”. It gives a measure of how a model is able to truly predict a response value for a given input. Often interpretability of the model decreases with an increase in the efficiency of the model.
This is due to the change in flexibility of the model and thus complex models can generate and map a wider range of possible input values. For example, KNN with k=1 is highly flexible when compared to a KNN with K=5. The selection of K is a highly subjective matter and is to be addressed as per the business application. Decreasing the value of “K” may give better accuracy but will decrease the interpretability of the model drastically.
3. Speed or Training time
Realistically, algorithms require more time to train on large training data. Higher the accuracy, the higher the training time. Also, In real-world applications, the choice of algorithm is driven by these two factors predominantly. ML Algorithms like Linear regression and Logistic regression take less time when compared to algorithms like SVM, Neural networks, random forests.
4. Linearity
Algorithms such as logistic regression and support vector machines assume that types can be separated by a straight line. Thus if the data is linear, then these algorithms have good performance.
For nonlinear data algorithms such as SVM, random forest, neural nets work well, as these ML Algorithms can handle nonlinearity and high dimensional complex data structures. The best way to find out the linearity is to try different algorithms.
5. Number of features
A dataset may have many parameters and for a certain business application, certain types of fields may be able to address it. Other features may not be relevant to the application and can create imbalance leading to inefficiency. When many features are important to the business and need to be considered, ML Algorithms such as SVM are better suited. Dimension reduction methods such as PCA, LDA can assist in selecting important and relevant features.
Some of the main aspects to consider when trying to solve a new problem are:
- The Objective of the problem.
- Categorize the problem.
- Understand Your Data.
- Find the appropriate ML Algorithms.
- Implementation of different machine learning algorithms.
- Optimizing of hyperparameters.
Conclusion
There is no hard and fast rule for selection. Every person may choose a different algorithm and support it. Business needs along with selecting appropriate ML Algorithms are very crucial. Choice of the algorithm is relative based on business requirements. Proper understanding of business needs and machine learning is the key to developing a successful model.
Written By: Jeet Barot
Reviewed By: Krishna Heroor
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