5 Machine Learning Models Explained in 5 Minutes

The five machine learning models highlighted in the article for their popularity and utility in various applications are:

1. Linear Regression: This algorithm is used to predict a continuous outcome (y) using one or more independent variables (X). It models the relationship between X and y, represented by y=mX+c.

2. Logistic Regression: A classification model used to predict a discrete outcome given one or more independent variables. It is modeled as an S-shape curve, where the x-axis represents the number of negative keywords, and the y-axis shows the probability of the email being spam.

3. Decision Trees: A popular machine learning model used for both classification and regression tasks. They work by breaking the dataset down based on its features, creating a tree-like structure to model this data.

4. Random Forests: The random forest model combines the predictions made by multiple decision trees and returns a single output. It performs better than a single decision tree because it leverages the capabilities of multiple predictive models.

5. K-Means Clustering: An unsupervised learning model that essentially ingests unlabeled data and assigns each data point to a cluster. The observations belong to the cluster with the nearest mean. It groups each data point into distinct clusters based on their proximity to the centroid.

Linear regression is a machine learning model used to perform quantitative tasks and predict a continuous outcome (y) using one or more independent variables (X). It works by fitting a linear equation that models the relationship between X and y, represented by y=mX+c. The algorithm finds the weights (W0 and W1) that lead to the best-fitting line for the input data (X). The best-fitting line is determined in terms of the lowest cost6.

An example of linear regression's application, as mentioned in the article, is predicting house prices based on their size. In this case, the house size is the independent variable (X) used to predict the house price (y). The algorithm learns the relationship between the house size and house price by fitting a linear equation to the data points.