How to train and evaluate multiple models efficiently

Introduction

A question all data scientists must confront when working on any machine learning project is…

Which model architecture is going to best fit my data?

There are several theoretical considerations one should take into account. For example, if your features show strong linear relationships to your dependent variable (target) then…

How to leverage unsupervised learning in your supervised learning problems

Introduction

Supervised classification problems require a dataset with (a) a categorical dependent variable (the “target variable”) and (b) a set of independent variables (“features”) which may (or may not!) be useful in predicting the class. The modeling task is to learn a function mapping features and their values to a target…

Cole Brendel

Data Scientist & Data Enthusiast

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