If you are new to data science, this title is not intended to insult you.
improve it through adjustments), the machine will use the model to learn the patterns in your data.
Then, you might input new conditions (observations) and it will predict the outcome!
Labeled simply means that the outcome of the observation (a.k.a.
the row of data) is known.
the variable you are interested in predicting) is made up of categories.
These categories could be yes/no, or something like a number between 1 and 10 representing customer satisfaction.
Linear Regression
Linear regression is often one of the first machine learning models that people learn.
This is because its algorithm (i.e.
This best-fit line is then used to make predictions about new data points (see illustration).
In fact, any model with a continuous target variable can be categorized as regression.
An example of a continuous variable would be the selling price of a house.
Linear regression is also very interpretable.
K Nearest Neighbors (KNN)
This model can be used for either classificationorregression!
The name K Nearest Neighbors is not intended to be confusing.
The model first plots out all of the data.
Whichever value receives the most votes is the value that KNN predicts for the new data point.
Thus, the model would predict class 1 for this data point.
line in the 2D case) and the majority of the other class falls on the other side.
the deep end, pun intended).
K means works best with clusters that are circular and of similar size.
DBSCAN will find the clusters for you!
Additionally, the DBSCAN model classifies noise points for you (i.e.
points that are far away from all other observations).
This model works better than K means when data points are very close together.
Neural networks
Neural networks are the coolest and most mysterious models, in my opinion.
They are calledneural networksbecause they are modeled after how the neurons in our brains work.
These models work to find patterns in the dataset; sometimes they find patterns that humans might never recognize.
Neural networks work well with complex data like images and audio.
Neural networks can be used with data that is labeled (i.e.
supervised learning applications) or data that is unlabeled (unsupervised learning) as well.
Even just touching the surface of how neural networks operate would likely be too complex for this article.
If you would like to learn more, here is a beginners guide:https://pathmind.com/wiki/neural-data pipe.
This article was originally published onTowards Data SciencebyMegan Dibble, an Industrial Engineer turned Data Scientist.
