A bundle of answers is on the internet which is the best ML Algorithm but varies depending upon the dataset. Actually, there are many suggestions & answers for every dataset, but some matrices are common. The type of problem, nature of the output, nature of the dataset, feature engineering, and overloading of training; all are the main factors.
The followings are the best guide to choosing the best machine learning (ML) algorithms:
Nature Of Training The Dataset
Commonly, it is a professional way to get more & more datasets for better and more robust results but how it is possible – this question may arise in your mind. However, there are two different scenarios
When Dataset is Small
If the number of rich features are very high (textual, acoustiic, visual ), low number of observation, then you can use the ML algorithms which has high bias or low variance. For example, Linear SVM, Linear Regression, and Naive Bayes.
When Dataset is Large
If the number of reich features are very low, observation time is higer, then it is gental decision to use low bias/ high variance ML algorithms. For instance, Decision Tree, KNN, Decision Tree etc.
Accuracy Measurement (Choose Model Intelligently)
Given dataset, having aproximation value after training determines the expcted prediciton which is very near to the actual results. Categories of Algorithm vary dependign upon the nature of dataset. Hence dataset is most important but how to choose the right one machine learning / deep learnind model for better results? Here are two different categoreis we can divide the nature:
- Highly Interpretable Algorithms
Those models whose nature of prediction is very close to a point where a human can understand the results. For example, Regression & Decision Tree
2. Flexible Models
The main benefit of these types of modle, you can increase the size of dataset. When deep insight into it gives the more accurate results because more number of features directly related to the best accuracy.
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