ML Models Pros & Cons
Quick refresher of fundamental Machine Learning models with Code Demonstrations for review.
Published in
5 min readOct 20, 2020
With the number of Machine Learning algorithms constantly growing it is nice to have a reference point to brush up on some of the fundamental models, be it for an interview or just a quick refresher. I wanted to provide a resource of some of the most common models pros and cons and sample code implementations of each of these algorithms in Python.
Table of Contents
- Multiple Linear Regression
- Logistic Regression
- k-Nearest Neighbors (KNN)
- k-Means Clustering
- Decision Trees/Random Forest
- Support Vector Machine (SVM)
- Naive Bayes
1. Multiple Linear Regression
Pros
- Easy to implement, theory is not complex, low computational power compared to other algorithms.
- Easy to interpret coefficients for analysis.
- Perfect for linearly separable datasets.
- Susceptible to overfitting, but can avoid using dimensionality reduction techniques, cross-validation, and regularization methods.