Contents
How do you know which algorithm is more efficient?
The standard way of comparing different algorithms is by comparing their complexity using Big O notation. In practice you would of course also benchmark the algorithms. As an example the sorting algorithms bubble sort and heap sort has complexity O(n2) and O(n log n) respective.
What is the criteria to decide that which algorithm is best?
Here are some important considerations while choosing an algorithm.
- Size of the training data. It is usually recommended to gather a good amount of data to get reliable predictions.
- Accuracy and/or Interpretability of the output.
- Speed or Training time.
- Linearity.
- Number of features.
How is a statistical test used to compare two algorithms?
If you do this for two algorithms, you can use a statistical test comparing the two means. For example, if you want to compare a logistic regression model with a random forest model. You could split the data into 10-folds and train 10 logistic regression models and 10 random forest models.
How to find any proposed algorithm is performing better?
If you could find theorically a formula for the speed of convergeance of your algorithm and compare with the formula for the existing one could be handy. The empiricals results should have significances but the formula for the speed of convergeance could be very convincing if you could also have the numbers of processors in the formula.
Which is the best measure of the provability of an algorithm?
Provability of the algorithm is an important measure for comparison. Check if your algorithm is deterministic. An algorithm that is easier to understand is always better because augmentation / modification to suit a particular requirement can be made without difficulty. Time and space complexity are vital and core.
How to compare the performance of your proposed ALG?
To compare the performance of your proposed alg. with existing algorithm, you should focus on some performance evalution parameters such as time complexity, space complexity, detection rate, accuracy, sensitivity , precision and recall ( in term of classification).