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Pros & Cons of the Most famous AI Algorithms
Latest   Machine Learning

Pros & Cons of the Most famous AI Algorithms

Last Updated on July 24, 2023 by Editorial Team

Author(s): Surya Govind

Originally published on Towards AI.

Pros & Cons of the Most famous AI Algorithms
Photo by Alex Knight on Unsplash

Famous AI weapons: what is the use, when to use & when to ignore. Point-wise summary


First, let’s see what a Classifier is: An algorithm that maps the input data to a specific category. Classification model: “A classification model tries to draw some conclusions from the input values given for training. It will predict the class labels/categories for the new data.” So here are a few algorithms mentioned below with a point-wise description. You will take an idea of when to use when to avoid, why you should use, etc.

1. Naive Bayes classifier:


  1. Simple, easy, and fast.
  2. Not sensitive to irrelevant features.
  3. Works great in practice.
  4. Needs less data during training.
  5. For both multiclass and binary classification.
  6. Works with continuous and discrete data.


1. Accept every feature is independent. This isn’t always the truth.

Decision Tree:


  1. Easy to understand.
  2. Easy to generate rules.
  3. There are almost hyperparameters to be tuned.
  4. Complex decision tree models can be significantly simplified by its visualization.


  1. Mine suffers from overfitting.
  2. It does not efficiently work with non-numerical data.
  3. No prediction accuracy for a data set in comparison with other algorithms.
  4. When there are many class labels, calculations can be complex.

Support Vector Machines:


  1. Fast algorithm.
  2. Effective in high dimensional spaces.
  3. Great accuracy.
  4. Power and flexibility from kernels.
  5. Works very well with the clear margin of separation.
  6. Many applications.


  1. It doesn’t perform well with large data sets.
  2. Not so simple to program.
  3. It doesn’t perform so well when the data comes with more noise, i.e. added classes are overlapping.

Random Forest Classifier:


  1. The overfitting problem does not exist.
  2. It can be used for feature engineering that is for identifying the most critical features among all available features in the training data set.
  3. Runs very well on large data set.
  4. Extraordinarily flexible and have very high accuracy.
  5. No need for operation of the input data.


  1. Complexity.
  2. It requires a lot of competition resources.
  3. Time-consuming.
  4. I need to choose the number of trees.

KNN Algorithm :


  1. Simple to understand and easy to implement.
  2. Zero two little train time.
  3. Works easily with multiclass data sets.
  4. Has good predictive power.
  5. It does well in practice.


  1. Computationally Expensive testing phase.
  2. It can have skewed class distribution.
  3. Accuracy can be decreased when it comes to high dimension data.
  4. Need to find a value for the parameter k.

I hope, now you have some idea about what are all these terms and when to use what! There are links added for every algorithm for your reference.

“It’s never too late to learn.”

Good Luck!

Happy AI Learning!

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