10 Best Machine Learning Algorithms For Data Scientists There is no such thing as the single best machine learning algorithm. Moreover, there are many ways to approach every problem.
Furthermore, very different data sets can require very different algo’s for the best outcome.
As a result, choosing between these models is a question of balancing performance, speed and of course avoiding over fitting.
Firstly, always start out with the more basic models for baseline models. In addition, move up the ladder of complexity depending on the task at hand.
Additionally, sometimes creating a good baseline model is the most difficult part. In conclusion, for the sake of creating a list for aspiring data scientists to start from, we have the following:
1. Logistic Regression
2. Linear Regression
3. Classification & Regression Trees
4. Linear Discriminant Analysis
5. K-Nearest Neighbors
6. Naive Bayes
7. Support Vector Machines
8. Learning Vector Quantization
9. Boosting and Adaboost
10. Bagging and Random Forest
Firstly, as a data scientist you must recognize that each algorithm works best for a certain type of data. Secondly, there is no such thing as a best algorithm. And, thirdly, knowing more algorithms is always better.
Moreover, ever data set is highly unique, like an individual snowflake. In conclusion, as you get comfortable with various algorithms, you will develop a sixth sense for applying the correct method.
10 Best Machine Learning Algorithms For Data Scientists