Decision Trees You can find the iPython Notebook for this post at https://github.com/bryantravissmith/FromScratch/tree/master/SupervisedLearning/DecisionTrees Decision Trees are a non-parametric supervised machine learning algorithm that takes a set of training data and constructs a set of regions in the space of features that is then used to make predictions. These predictions can be continuous values (Decision Tree […]

# Tag Archives: Theory

## Implementing Decision Trees From Scratch Using R

Decision Trees You can find the iPython Notebook for this post at https://github.com/bryantravissmith/FromScratch/tree/master/SupervisedLearning/DecisionTrees Decision Trees are a non-parametric supervised machine learning algorithm that takes a set of training data and constructs a set of regions in the space of features that is then used to make predictions. These predictions can be continuous values (Decision Tree […]

## Regularization in Logistic Regression

Regularization Regularization is a mathematical method to add constraints to an expression of a problem. In context of machine learning, it is a way to encourage the reduction of model complexity, reduce overfitting, or/and establish priors on the weights/coefficients of the model. I want to focus on the last part about priors for this post. […]

## Implementing Logistic Regression From Scratch – Part 1: Theory

Logistic Regression is a common Supervised Machine Learning Classification algorithm that attempts to estimate the probability that a given set of data can be classified as a positive example of some category. Examples of some question logistic regression can help answer include: What is the probability a costumer will purchase a product given their […]