5 ML models every data scientist should know
Sometimes the hardest part about machine learning is figuring out where to start. ML has made waves in the past few years, and the options for what models to use are ever-growing. Many times there isn’t one “right” model for the problem you’re trying to solve, so it can be helpful to be familiar with several options. With so many factors such as size, quality, and type of data, it’s important to be familiar with different models, so you know the algorithm that aligns best with your needs. Here are five general algorithms that help provide a basic understanding of ML and help you find the perfect place to start.
Logistic regression model
In terms of regression analysis, logistic regression models estimate the parameters of a logistic model. The most common models are structured for binary outcomes (yes/no). Logistic regression models are used to consider the probability of an event taking place with log odds combined with 1+ independent variables. Logistic models are helpful for classification problems where you are trying to determine the best fit.
Decision Tree Model
Decision Tree models are a subset of supervised machine learning. A classification model reads input and generates a classification that collects the data into a category. An example of binary…
